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Hernández-Stefanoni, José, Gabriela Reyes-Palomeque, Miguel Castillo-Santiago, Stephanie George-Chacón, Astrid Huechacona-Ruiz, Fernando Tun-Dzul, Dinosca Rondon-Rivera, and Juan Dupuy. "Effects of Sample Plot Size and GPS Location Errors on Aboveground Biomass Estimates from LiDAR in Tropical Dry Forests." Remote Sensing 10, no. 10 (October 2, 2018): 1586. http://dx.doi.org/10.3390/rs10101586.
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Accurate estimates of above ground biomass (AGB) are needed for monitoring carbon in tropical forests. LiDAR data can provide precise AGB estimations because it can capture the horizontal and vertical structure of vegetation. However, the accuracy of AGB estimations from LiDAR is affected by a co-registration error between LiDAR data and field plots resulting in spatial discrepancies between LiDAR and field plot data. Here, we evaluated the impacts of plot location error and plot size on the accuracy of AGB estimations predicted from LiDAR data in two types of tropical dry forests in Yucatán, México. We sampled woody plants of three size classes in 29 nested plots (80 m2, 400 m2 and 1000 m2) in a semi-deciduous forest (Kiuic) and 28 plots in a semi-evergreen forest (FCP) and estimated AGB using local allometric equations. We calculated several LiDAR metrics from airborne data and used a Monte Carlo simulation approach to assess the influence of plot location errors (2 to 10 m) and plot size on ABG estimations from LiDAR using regression analysis. Our results showed that the precision of AGB estimations improved as plot size increased from 80 m2 to 1000 m2 (R2 = 0.33 to 0.75 and 0.23 to 0.67 for Kiuic and FCP respectively). We also found that increasing GPS location errors resulted in higher AGB estimation errors, especially in the smallest sample plots. In contrast, the largest plots showed consistently lower estimation errors that varied little with plot location error. We conclude that larger plots are less affected by co-registration error and vegetation conditions, highlighting the importance of selecting an appropriate plot size for field forest inventories used for estimating biomass.
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Gobakken, Terje, and Erik Næsset. "Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data." Canadian Journal of Forest Research 39, no. 5 (May 2009): 1036–52. http://dx.doi.org/10.1139/x09-025.
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Canopy height distributions were created from small-footprint airborne laser scanner data with an average sampling density of 1.1 points·m–2 collected over 132 sample plots and 61 stands. Field measurements of each plot were carried out within two concentric circles (200 m2 and 300 or 400 m2). The plot positions were altered randomly with Monte Carlo simulations. For various metrics derived from the canopy height distributions, the mean and the standard deviation (SD) of the differences between incorrect plot positions and ground-truth positions were compared. In general, SD was smaller for large field plots than for small plots, and the variation in SD among the Monte Carlo repetitions was smaller for large sample plots. The combined effects of field plot size and sample plot position error on the accuracy of mean tree height (hL), stand basal area (G), and stand volume (V) predicted at stand level using a two-stage procedure combining field training data and laser data were assessed. Standard deviation of the differences between predicted and observed hL was quite stable and of similar size for position errors up to 5 m. However, for G and V the influence of plot position error was more pronounced.
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Behrens, John T., William A. Stock, and Catherine Sedgwick. "Judgment Errors in Elementary Box-Plot Displays." Communications in Statistics - Simulation and Computation 19, no. 1 (January 1990): 245–62. http://dx.doi.org/10.1080/03610919008812855.
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Schwemmle, Robin, Dominic Demand, and Markus Weiler. "Technical note: Diagnostic efficiency – specific evaluation of model performance." Hydrology and Earth System Sciences 25, no. 4 (April 22, 2021): 2187–98. http://dx.doi.org/10.5194/hess-25-2187-2021.
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Abstract. A better understanding of the reasons why hydrological model performance is unsatisfying represents a crucial part of meaningful model evaluation. However, current evaluation efforts are mostly based on aggregated efficiency measures such as Kling–Gupta efficiency (KGE) or Nash–Sutcliffe efficiency (NSE). These aggregated measures provide a relative gradation of model performance. Especially in the case of a weak model performance it is important to identify the different errors which may have caused such unsatisfactory predictions. These errors may originate from the model parameters, the model structure, and/or the input data. In order to provide more insight, we define three types of errors which may be related to their source: constant error (e.g. caused by consistent input data error such as precipitation), dynamic error (e.g. structural model errors such as a deficient storage routine) and timing error (e.g. caused by input data errors or deficient model routines/parameters). Based on these types of errors, we propose the novel diagnostic efficiency (DE) measure, which accounts for these three error types. The disaggregation of DE into its three metric terms can be visualized in a plain radial space using diagnostic polar plots. A major advantage of this visualization technique is that error contributions can be clearly differentiated. In order to provide a proof of concept, we first generated time series artificially with the three different error types (i.e. simulations are surrogated by manipulating observations). By computing DE and the related diagnostic polar plots for the reproduced errors, we could then supply evidence for the concept. Finally, we tested the applicability of our approach for a modelling example. For a particular catchment, we compared streamflow simulations realized with different parameter sets to the observed streamflow. For this modelling example, the diagnostic polar plot suggests that dynamic errors explain the overall error to a large extent. The proposed evaluation approach provides a diagnostic tool for model developers and model users and the diagnostic polar plot facilitates interpretation of the proposed performance measure as well as a relative gradation of model performance similar to the well-established efficiency measures in hydrology.
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Fortin, Mathieu, Josianne DeBlois, Sylvain Bernier, and Georges Blais. "Mise au point d'un tarif de cubage général pour les forêts québécoises : une approche pour mieux évaluer l'incertitude associée aux prévisions." Forestry Chronicle 83, no. 5 (September 1, 2007): 754–65. http://dx.doi.org/10.5558/tfc83754-5.
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Merchantable volume assessment is of prime importance in forest management and for the estimation of wood production in Quebec Crown forests. Currently, this assessment is undertaken at the individual stem level according to a statistical model commonly identified as the Perron general stock table. This polynomial model is based on tree diameter at breast height and tree height. However, the mathematical model form and the method used to calibrate it do not enable a correct and detailed assessment of the uncertainty associated with volume assessments. This study describes a new model which, accounts for errors associated with the use of estimated height in volume forecasts and also limits the propogation of errors to sample plot and cruise line. Random effects have been specified in the model in order to take into account spatial correlation between observations made at the sample plot and cruise line level. Results indicate sample plot and cruise line random errors constitute components of model error, which individually range from 2 % to 4 % of volume assessment. Consequently, the basic premise that errors associated with volume assessment of individual stems are compensated by volume summations at the sample plot level is not valid. Key words: mixed model, random effect, error propagation, variance
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SINGH, M., and M. PALA. "Use of covariance structures for temporal errors in the analysis of a three-course wheat rotation and tillage trial." Journal of Agricultural Science 142, no. 2 (April 2004): 193–201. http://dx.doi.org/10.1017/s0021859604004174.
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Crop rotation serves as a mechanism for developing sustainable crop production systems. Crop-rotation trials are used to identify agronomic input factors suitable for use in a cropping system. In crop-rotation trials, experimental errors within the same plot over time are correlated. The form of the covariance structure of the plot errors may be specific to the data from a rotation trial, but is unknown and is generally assumed. Statistical analyses are usually based on the assumption that plot errors are independent, or have constant covariance. An experiment was conducted using wheat-based, three-course rotations containing tillage treatment subplots over 12 years at ICARDA's experimental station at Tel Hadya, a moderately dry area in northern Syria. This study examined several covariance structures for temporal errors arising over the rotation plots and tillage subplots, in order to model wheat yield data. Eighteen covariance structures were examined, and the best pair was selected using the Akaike Information Criterion. The best pair comprised first-order autocorrelation and homogeneous variance for temporal errors in rotation plots, and uniform correlation with heterogeneous variances for temporal errors in tillage subplots. Using the 12 years of data obtained for wheat yield and the best pair of covariance structures, the tillage and rotation effects were found to be statistically significant and to have significant interactions with the cycle of rotation. The precision of the means calculated differed from those calculated using a control structure based on homogeneous error variances and constant correlation. The cumulative yield build-up over time differed significantly over the rotations and the tillage methods. An increasing yield trend was observed for the bread wheat rotation, while a yield decline was observed in durum wheat when the rotation was repeated. When evaluating the effects of input factors in crop rotations, we therefore recommend that the covariance structures be examined and that a suitably chosen structure be used.
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Mauro, F., R. Valbuena, J. A. Manzanera, and A. García-Abril. "Influence of Global Navigation Satellite System errors in positioning inventory plots for tree-height distribution studiesThis article is one of a selection of papers from Extending Forest Inventory and Monitoring over Space and Time." Canadian Journal of Forest Research 41, no. 1 (January 2011): 11–23. http://dx.doi.org/10.1139/x10-164.
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Validation of predictive models in remote sensing requires a good coregistration of field and sensor data sets. However, previous research has demonstrated that Global Navigation Satellite System survey techniques often produce large positioning errors when applied to areas under forest canopies. In this article, we present a repeatable methodology for analyzing the effect of such errors when validating models that predict tree-height distributions from LiDAR data sets. The method is based on conditional probability theory applied to error positioning and includes an error assessment of the surveying technique. A technical criterion for selecting the plot radius that avoids significant effects of positioning errors was proposed. We demonstrated that for a plot radius greater than 10 m, the effects of positioning errors introduced by a phase-differential device were insignificant when studying forest tree-height distributions.
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Van Deusen, Paul C. "Forest inventory estimation with mapped plots." Canadian Journal of Forest Research 34, no. 2 (February 1, 2004): 493–97. http://dx.doi.org/10.1139/x03-209.
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Procedures are developed for estimating means and variances with a mapped-plot design. The focus is on fixed-area plots, and simulations are used to validate the proposed estimators. The mapped-plot estimators for means and variances are compared with simple random sampling estimators that utilize only full plots. As expected, the mapped-plot estimates have smaller mean squared errors than the simple random sampling estimates. The theory for fixed-area plots is easy to apply, although additional work is required to map plots in the field. Corresponding theory for variable plots is developed but not tested with simulations. The difficulty of applying these methods to variable plots is greater, but not prohibitive.
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Sabor, Alexia A., Volker C. Radeloff, Ronald E. McRoberts, Murray Clayton, and Susan I. Stewart. "Adding uncertainty to forest inventory plot locations: effects on analyses using geospatial data." Canadian Journal of Forest Research 37, no. 11 (November 2007): 2313–25. http://dx.doi.org/10.1139/x07-067.
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The Forest Inventory and Analysis (FIA) program of the USDA Forest Service alters plot locations before releasing data to the public to ensure landowner confidentiality and sample integrity, but using data with altered plot locations in conjunction with other spatially explicit data layers produces analytical results with unknown amounts of error. We calculated the potential error from using altered location data in combination with other data layers that varied in mean map unit size. The incidence of errors associated with the use of altered plot locations exhibited a strong inverse relationship to the mean map unit size of the other data sets used in the analyses. For a 30 m × 30 m resolution land cover map, plot misclassification rates ranged from 32% to 66%, whereas only 1%–10% of plots were misclassified for ecological subsection data (mean polygon size 9067 km2). Housing density data derived from the US Decennial Census (mean polygon size = 5.7 km2) represented an intermediate condition, with 5%–70% of data points misclassified when altered plot locations were used. These analyses demonstrate the impacts of altering FIA plot locations and represent an important step toward making the FIA database more helpful to a broad variety of end users.
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AKIMOTO, Naoshige, Hisako HANAKUMA, and Keiichiro HOZUMI. "Errors in acid-base titration using Gran's plot method." Analytical Sciences 3, no. 6 (1987): 515–19. http://dx.doi.org/10.2116/analsci.3.515.
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Grimsbo Jewett, Jane, Craig C. Sheaffer, Roger D. Moon, and JoAnn F. S. Lamb. "Field sampling strategies for studies of alfalfa forage quality." Canadian Journal of Plant Science 81, no. 4 (October 1, 2001): 703–12. http://dx.doi.org/10.4141/p00-040.
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Information is scarce on sampling techniques for field studies of alfalfa forage quality. Standard formulas are available for estimating the number of samples needed for reducing error in a study, but little is known about the impact of plot sampling on forage quality. Our objectives were to compare the strategy of manual harvesting from small areas within plots with that of grab sampling mechanically harvested forage, and to determine whether the within-plot location of sampling affected forage quality in any systematic way. Alfalfa forage was sampled from swaths of mechanically clipped forage (grab samples) and from hand-clipped areas within field plots (area samples). Systematic sample location within a plot had no discernable effect on forage quality. Calculations of predicted standard errors and required sample numbers indicated that one area or one grab sample per plot with three replicates would provide an acceptable standard error for comparison of alfalfa entries for protein and fiber concentration. Within-plot variability was greater at late-summer harvests than earlier harvests, but at all harvests one sample per plot with three replicates gave adequate precision for forage quality comparisons. Higher forage quality from grab samples than from area samples at spring harvests suggested the need for caution when comparing forage quality studies done with different harvest methods; however, there were few entry × sampling strategy interactions, which suggests that relative performance of entries would be similar regardless of the method of sampling. Key words: Alfalfa, forage quality, Medicago sativa L.; field sampling, bootstrap
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Odell, Daniel, Adam M. Dubis, Jackson F. Lever, Kimberly E. Stepien, and Joseph Carroll. "Assessing Errors Inherent in OCT-Derived Macular Thickness Maps." Journal of Ophthalmology 2011 (2011): 1–9. http://dx.doi.org/10.1155/2011/692574.
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SD-OCT has become an essential tool for evaluating macular pathology; however several aspects of data collection and analysis affect the accuracy of retinal thickness measurements. Here we evaluated sampling density, scan centering, and axial length compensation as factors affecting the accuracy of macular thickness maps. Forty-three patients with various retinal pathologies and 113 normal subjects were imaged using Cirrus HD-OCT. Reduced B-scan density was associated with increased interpolation error in ETDRS macular thickness plots. Correcting for individual differences in axial length revealed modest errors in retinal thickness maps, while more pronounced errors were observed when the ETDRS plot was not positioned at the center of the fovea (which can occur as a result of errant fixation). Cumulative error can exceed hundreds of microns, even under “ideal observer” conditions. This preventable error is particularly relevant when attempting to compare macular thickness maps to normative databases or measuring the area or volume of retinal features.
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Fortin, Mathieu, and Josianne DeBlois. "A statistical estimator to propagate height prediction errors into a general volume model." Canadian Journal of Forest Research 40, no. 10 (October 2010): 1930–39. http://dx.doi.org/10.1139/x10-107.
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In most large-scale inventories, volume models rely on predicted heights instead of measured heights. The effect of height predictions over volume estimate uncertainty is therefore a crucial issue. In this study, we designed a statistical estimator that enables the propagation of tree height prediction errors into a general volume model. The proposed estimator relies on the assumption that both the volume model and the height–diameter model that provides the height predictions do not exhibit any lack of fit. This estimator was tested against a Monte Carlo simulation for 90 plots from three typical forest types in Quebec, Canada (hardwood, mixedwood, and softwood). The results show that the estimator provides plot volume predictions with error variances that fit those estimated using the Monte Carlo simulation. The estimator developed in this study may be useful to quickly provide the uncertainty associated with plot volume predictions in large-scale inventories.
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S.I, Alhassan, Sule S.Y, Murtala R., Rasheed H.I, Baso A.A, Sheshe F.A, and Jafar S.C. "Comparative viscometric study of pure and acetylated gum Arabic using different plot methods." International Journal of Advanced Chemistry 8, no. 1 (June 4, 2020): 128. http://dx.doi.org/10.14419/ijac.v8i1.30526.
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Acetylation of gum Arabic was achieved using acetic anhydride as solvent. The ester group formed was confirmed by FTIR spectra having absorption band of 750 cm-1 – 700 cm-1. Viscometric study of the pure and acetylated samples was carried out. Relative viscosity of acetylated gum was found to be higher than that of the pure gum. Intrinsic viscosity was determined for the two samples using different plot methods taking Huggin’s plot as standard. The intrinsic viscosity was found to be 86.43 cm3/g and 64.59 cm3/g for acetylated and pure gum arabic respectively. Relative errors of other methods for the two samples was compared to that of Huggins and the plots that are most comparable to Huggins with relative errors less than 5% are; Martin, Lyon-Tobolsky, Staudinger-Heuer, Maron-Reznik and our proposed method. The proposed method which was a modification of the Kreiser method gave relative error less than 2 %, for both pure and modified gum. Whereas the Kreiser method gave relative error greater than 15 % for both methods. The critical concentration for the samples was found to be 0.0116 g/cm3 and 0.0155 g/cm3 for acetylated and pure gum respectively. This shows that there was no molecule-molecule entanglements during viscosity measurements.
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Dietze, Michael, Sebastian Kreutzer, Christoph Burow, Margret C. Fuchs, Manfred Fischer, and Christoph Schmidt. "The abanico plot: Visualising chronometric data with individual standard errors." Quaternary Geochronology 31 (February 2016): 12–18. http://dx.doi.org/10.1016/j.quageo.2015.09.003.
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Nanos, Nikos, and Sara Sjöstedt de Luna. "Fitting diameter distribution models to data from forest inventories with concentric plot design." Forest Systems 26, no. 2 (July 10, 2017): e01S. http://dx.doi.org/10.5424/fs/2017262-10486.
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Aim: Several national forest inventories use a complex plot design based on multiple concentric subplots where smaller diameter trees are inventoried when lying in the smaller-radius subplots and ignored otherwise. Data from these plots are truncated with threshold (truncation) diameters varying according to the distance from the plot centre. In this paper we designed a maximum likelihood method to fit the Weibull diameter distribution to data from concentric plots.Material and methods: Our method (M1) was based on multiple truncated probability density functions to build the likelihood. In addition, we used an alternative method (M2) presented recently. We used methods M1 and M2 as well as two other reference methods to estimate the Weibull parameters in 40000 simulated plots. The spatial tree pattern of the simulated plots was generated using four models of spatial point patterns. Two error indices were used to assess the relative performance of M1 and M2 in estimating relevant stand-level variables. In addition, we estimated the Quadratic Mean plot Diameter (QMD) using Expansion Factors (EFs).Main results: Methods M1 and M2 produced comparable estimation errors in random and cluster tree spatial patterns. Method M2 produced biased parameter estimates in plots with inhomogeneous Poisson patterns. Estimation of QMD using EFs produced biased results in plots within inhomogeneous intensity Poisson patterns.Research highlights:We designed a new method to fit the Weibull distribution to forest inventory data from concentric plots that achieves high accuracy and precision in parameter estimates regardless of the within-plot spatial tree pattern.
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Nakajima, Haruki. "Plot location errors of National Forest Inventory: related factors and adverse effects on continuity of plot data." Journal of Forest Research 21, no. 6 (August 18, 2016): 300–305. http://dx.doi.org/10.1007/s10310-016-0538-1.
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Darban, Ameneh, Mojtaba Ghaedi, and Jafar Qajar. "Analysis of the impacts of relative permeability and mobility ratio on heterogeneity loss error during upscaling of geological models." Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles 75 (2020): 53. http://dx.doi.org/10.2516/ogst/2020049.
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The detailed geological fine grids are upscaled to create reliably sized simulation coarse models to solve flow equations in a more efficient way. Any upscaling process results in a loss of accuracy, along with an increase of errors. Numerical dispersion, heterogeneity loss, and connectivity misrepresentation are responsible for the upscaling errors. Recognizing the source of each error, and the behavior of influential factors through upscaling process could provide an optimum level of upscaling and an evaluation of upscaling methods’ accuracy. Despite the importance of upscaling error, little attention has been paid to this subject. This paper represents a rigorous analysis of the heterogeneity loss behavior associated with the relative permeability contrast and the mobility ratio under a waterflooding process. For this purpose, heterogeneous fine grid models are constructed by the fractional Brownian motion process. The models are upscaled by three upscaling factors. The models achieved are implemented to eliminate the impact of numerical error among upscaling errors in order to focus strictly on heterogeneity loss. Water–oil displacement simulation is then performed on fine and corresponding refined upscaled models at three different ratios of relative permeabilities and mobility ratios. In the next stage, the relation between flow performance error and heterogeneity loss is investigated by the heterogeneity loss plot. The slope of this plot provides the reservoir engineer an insight to evaluate the performance of upscaling methods and the behavior of the influential factors on upscaling errors. Moreover, by using the heterogeneity loss plot for each ratio, a limit of coarsening is presented. Based on the results, the heterogeneity loss error is affected more by the mobility ratio contrast than the relative permeability difference. Also, it is demonstrated that water-wet reservoirs with light oil are more sensitive to the level of upscaling.
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Taffé, Patrick. "Effective plots to assess bias and precision in method comparison studies." Statistical Methods in Medical Research 27, no. 6 (October 4, 2016): 1650–60. http://dx.doi.org/10.1177/0962280216666667.
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Bland and Altman’s limits of agreement have traditionally been used in clinical research to assess the agreement between different methods of measurement for quantitative variables. However, when the variances of the measurement errors of the two methods are different, Bland and Altman’s plot may be misleading; there are settings where the regression line shows an upward or a downward trend but there is no bias or a zero slope and there is a bias. Therefore, the goal of this paper is to clearly illustrate why and when does a bias arise, particularly when heteroscedastic measurement errors are expected, and propose two new plots, the “bias plot” and the “precision plot,” to help the investigator visually and clinically appraise the performance of the new method. These plots do not have the above-mentioned defect and still are easy to interpret, in the spirit of Bland and Altman’s limits of agreement. To achieve this goal, we rely on the modeling framework recently developed by Nawarathna and Choudhary, which allows the measurement errors to be heteroscedastic and depend on the underlying latent trait. Their estimation procedure, however, is complex and rather daunting to implement. We have, therefore, developed a new estimation procedure, which is much simpler to implement and, yet, performs very well, as illustrated by our simulations. The methodology requires several measurements with the reference standard and possibly only one with the new method for each individual.
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Xie, Yuyang, Tao Yang, Xiaofeng Wang, Xi Chen, Shuxin Pang, Juan Hu, Anxian Wang, Ling Chen, and Zehao Shen. "Applying a Portable Backpack Lidar to Measure and Locate Trees in a Nature Forest Plot: Accuracy and Error Analyses." Remote Sensing 14, no. 8 (April 8, 2022): 1806. http://dx.doi.org/10.3390/rs14081806.
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Accurate tree positioning and measurement of structural parameters are the basis of forest inventory and mapping, which are important for forest biomass calculation and community dynamics analyses. Portable backpack lidar that integrates the simultaneous localization and mapping (SLAM) technique with a global navigation satellite system receiver has greater flexibility for tree inventory than terrestrial laser scanning, but it has never been used to measure and map forest structure in a large area (>101 hectares) with high tree density. In the present study, we used the LiBackpack DG50 backpack lidar system to obtain the point cloud data of a 10 ha plot of subtropical evergreen broadleaved forest, and applied these data to quantify errors and related factors in the diameter at breast height (DBH) measurements and positioning for more than 1900 individual trees. We found an average error of 4.19 cm in the DBH measurements obtained by lidar, compared with manual field measurements. The incompleteness of the tree stem point clouds was the main factor that caused the DBH measurement errors, and the field DBH measurements and density of the point clouds also had significant impacts. The average tree positioning error was 4.64 m, and it was significantly affected by the distance and route length from the measured trees to the data acquisition start position, whereas it was affected little by the habitat complexity and characteristics of tree stems. The tree positioning measurement error led to increases in the mean value and variability of paired-tree distance error as the sample plot scale increased. We corrected the errors based on the estimates of predictive models. After correction, the DBH measurement error decreased by 31.3%, the tree positioning error decreased by 44.3%, and the paired-tree distance error decreased by 56.3%. As the sample plot scale increased, the accumulated paired-tree distance error stabilized gradually.
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Torres-Contreras, Ignacio, Juan Carlos Jáuregui-Correa, Carlos Santiago López-Cajún, and Salvador Echeverría-Villagómez. "Effects of Phase Shift Errors in Recurrence Plot for Rotating Machinery Fault Diagnosis." Applied Sciences 11, no. 2 (January 19, 2021): 873. http://dx.doi.org/10.3390/app11020873.
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For fault diagnosis and predictive maintenance of rotating machinery, the phase errors generated by the integration processing of a vibration signal are an essential investigation subject. Phase errors affect the solution of mechanical systems with multiple vibration sources and also the information transmitted through the vibration that is used for fault diagnosis. This work proposes the use of phase plane, recurrence plot (RP), and cross recurrence plot (CRP) to evaluate phase shift error effects on the solution of multiple asynchronous and simple periodic functions, and on the smoothing of a Gaussian peak with white noise. Noisy peaks were smoothed twice with the triangular method and with a different number of points. The analysis of the asynchronous periodic functions and the smoothing indicated that a small phase shift changes the phase plane and the RP pattern. These changes can affect not only the accuracy of machinery fault diagnosis but also prediction for the application of timely maintenance actions.
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Hong, Chuan, Rui Duan, Lingzhen Zeng, Rebecca A. Hubbard, Thomas Lumley, Richard D. Riley, Haitao Chu, Stephen E. Kimmel, and Yong Chen. "The Galaxy Plot: A New Visualization Tool for Bivariate Meta-Analysis Studies." American Journal of Epidemiology 189, no. 8 (January 13, 2020): 861–69. http://dx.doi.org/10.1093/aje/kwz286.
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Abstract Funnel plots have been widely used to detect small-study effects in the results of univariate meta-analyses. However, there is no existing visualization tool that is the counterpart of the funnel plot in the multivariate setting. We propose a new visualization method, the galaxy plot, which can simultaneously present the effect sizes of bivariate outcomes and their standard errors in a 2-dimensional space. We illustrate the use of the galaxy plot with 2 case studies, including a meta-analysis of hypertension trials with studies from 1979–1991 (Hypertension. 2005;45(5):907–913) and a meta-analysis of structured telephone support or noninvasive telemonitoring with studies from 1966–2015 (Heart. 2017;103(4):255–257). The galaxy plot is an intuitive visualization tool that can aid in interpreting results of multivariate meta-analysis. It preserves all of the information presented by separate funnel plots for each outcome while elucidating more complex features that may only be revealed by examining the joint distribution of the bivariate outcomes.
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Magnussen, S., E. Næsset, and T. Gobakken. "LiDAR-supported estimation of change in forest biomass with time-invariant regression models." Canadian Journal of Forest Research 45, no. 11 (November 2015): 1514–23. http://dx.doi.org/10.1139/cjfr-2015-0084.
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A single a priori chosen linear regression model with two alternative error structures is proposed for model-assisted (MA) and model-dependent (MD) estimation of state and change in aboveground tree biomass (AGB, Mg·ha−1) in three forest strata in the Våler forest in southeastern Norway. Field data of tree height and stem diameter were collected in 145 permanent 200 m2circular plots. Concurrent LiDAR data were collected for the entire forest. The regression model includes two LiDAR-based explanatory variables: the mean of canopy height raised to a power of 1.5 and the standard deviation of canopy heights. A nearest-neighbour thinning of the 2010 LiDAR data to the density of the 1999 data was implemented to counter density effects in the explanatory variables. Estimates of change based on a single regression model were more accurate than estimating change from year-specific models (and no data thinning). A canopy height dependent correlated error structure was preferred over a partitioning of the error to temporary and “permanent” plot effects. For point estimates of AGB in 1999 and 2010, MA and MD estimates of errors were numerically comparable, but MD errors of change were much smaller than corresponding MA errors.
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Williams, P. J. S., A. Etemadi, I. W. McCrea, and H. Todd. "Errors due to random noise in velocity measurement using incoherent-scatter radar." Annales Geophysicae 14, no. 12 (December 31, 1996): 1480–86. http://dx.doi.org/10.1007/s00585-996-1480-x.
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Abstract. The random-noise errors involved in measuring the Doppler shift of an 'incoherent-scatter' spectrum are predicted theoretically for all values of Te/Ti from 1.0 to 3.0. After correction has been made for the effects of convolution during transmission and reception and the additional errors introduced by subtracting the average of the background gates, the rms errors can be expressed by a simple semi-empirical formula. The observed errors are determined from a comparison of simultaneous EISCAT measurements using an identical pulse code on several adjacent frequencies. The plot of observed versus predicted error has a slope of 0.991 and a correlation coefficient of 99.3%. The prediction also agrees well with the mean of the error distribution reported by the standard EISCAT analysis programme.
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Becker, Peter, and Tom Nichols. "Effects of Basal Area Factor and Plot Size on Precision and Accuracy of Forest Inventory Estimates." Northern Journal of Applied Forestry 28, no. 3 (September 1, 2011): 152–56. http://dx.doi.org/10.1093/njaf/28.3.152.
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Abstract We tested the effects of plot size (0.05-0.30 ac) and basal area factor (BAF) (5-30) on the accuracy and precision of per-acre estimates of tree number, basal area, biomass (all for trees ≥4.5 in. dbh), and sawtimber volume (for trees ≥11.6 in. dbh). Field sampling errors, such as missing in-trees, did not affect our tests. Virtual variable- and fixed-radius plots were randomly located within an artificial matrix of 130 real plots in well-stocked upland hardwood forests of sawtimber-sized trees in the Missouri Ozarks. Inventory parameters were essentially independent of plot size and BAF, whereas their coefficients of variation decreased with plot size and increased with BAF. Thus, our results for random plots agreed with sampling theory, unlike a previous study using concentric virtual plots in West Virginia forests. A very concentrated zone of high tree density around some plot centers apparently caused the biased estimates by concentric plots. Compared with the entire composite forest, inventory means were accurately estimated (to within 5%) and size class distributions were well represented for plots ≥0.1 ac or ≤15 BAF. Our procedures provide a basis for selecting an efficient and cost-effective sampling design suited to forest characteristics and the inventory's purpose.
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Dugan, Alexa J., and William L. Baker. "Modern calibration and historical testing of small-area, fire-interval reconstruction methods." International Journal of Wildland Fire 23, no. 1 (2014): 58. http://dx.doi.org/10.1071/wf12162.
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Accuracy of small-area, fire-interval estimation methods has been inadequately assessed, thus we conducted modern calibration and historical testing of the traditional composite-fire-interval and a newer all-tree-fire-interval method for estimating population mean fire intervals. We tested in eight areas, at four scales, using 30 small plots across ponderosa pine forests on the South Rim of Grand Canyon National Park. In modern calibration, individual-plot all-tree-fire-intervals were equal to population mean fire intervals in all plots. Across the eight areas, a mean-plot version of the all-tree-fire-interval method never failed, whereas mean-plot versions of composite-fire-intervals failed in 37.5–100% of areas. Pooled composite-fire-intervals, the traditional method, failed in all subareas. In historical testing, pooled and mean-plot all-tree-fire-interval methods and two variations of a mean-plot composite-fire-interval method had the lowest mean relative errors. Again, pooled composite-fire-intervals performed poorly across the eight areas. Overall, in modern and historical tests, the mean-plot all-tree-fire-interval method outperformed all others, but highly filtered mean-plot composite-fire-intervals were fairly accurate in historical tests. Both could be reliable methods, if replicated in small plots averaged over 600–1000-ha landscapes, but for small areas, the all-tree-fire-interval method outperformed others. However, for general use, there may be more value in spatially explicit, landscape-scale methods, rather than any small-area method.
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Shao, J., W. Zhang, L. Luo, S. Cai, and H. Jiang. "SLAM-BASED BACKPACK LASER SCANNING FOR FOREST PLOT MAPPING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 267–71. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-267-2020.
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Abstract. Acquisition of three-dimensional (3D) structural information is significant for forest measurements. To achieve faster data collection in forests, we design a backpack laser scanning (BLS) system using a single mobile laser scanning (MLS) scanner and specific to forest environments. The simultaneous localization and mapping (SLAM) approach based on the natural geometric characteristics of trees is used for BLS-based forest mapping, in which the skeleton line of the individual tree is extracted for scan matching and the incremental maps are adopted for global optimization of all the BLS point clouds. The final experimental results show that the SLAM-based BLS system achieves accurate forest plots mapping and allows reaching low mapping errors, in which the mean errors are approximately 3 cm in the horizontal and 2 cm in the vertical direction.
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Taffé, Patrick, Mingkai Peng, Victoria Stagg, and Tyler Williamson. "MethodCompare: An R package to assess bias and precision in method comparison studies." Statistical Methods in Medical Research 28, no. 8 (February 28, 2018): 2557–65. http://dx.doi.org/10.1177/0962280218759693.
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Bland and Altman’s limits of agreement have been used in many clinical research settings to assess agreement between two methods of measuring a quantitative trait. However, when the variances of the measurement errors of the two methods are different, limits of agreement can be misleading. MethodCompare is an R package that implements a new statistical methodology, developed by Taffé in 2016. MethodCompare produces three new plots, the “bias plot”, the “precision plot”, and the “comparison plot” to visually evaluate the performance of the new measurement method against the reference method. The method is illustrated on three simulated examples. Note that the Taffé method assumes that there are several measurements from reference standard and possibly as few as one measurement from the new method for each individual.
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Edwards, Francis. "Still Investigating Gunpowder Plot." Recusant History 21, no. 3 (May 1993): 305–46. http://dx.doi.org/10.1017/s0034193200001655.
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Dr. Mark Nicholls’sInvestigating Gunpowder Plotpublished in May, 1991, is a useful reminder thatGuy Fawkes: the Real Story of the Gunpowder Plot?,to name nothing else published in or near 1969, has by no means found universal acceptance as the more probable solution of the confused evidence surrounding this bizarre episode. This is not surprising. The last mentioned book was written for a wide audience. Thanks no doubt in large measure to the kindly influence of Lady Antonia Fraser it was chosen as the Book of the Month by PUNCH all those years ago. Evidently, such a choice, however desirable in itself, could not be regarded as the highest of recommendations by the world of Academe. Nevertheless, it must be insisted that the book was based on extensive research not only in this country but also abroad, notably in Rome, Florence, Spain, Holland and Belgium. Errors occurred which will need to be corrected in any future edition or new work. Dr. Nicholls, while attempting to refute the basic thesis, was fair enough to admit, ‘such views have found perhaps their most determined and skilful advocate in Fr. Francis Edwards’. But one hopes that an opposing view is based on more than skill, which could be mere forensic adroitness, and on determination which could be another word for obstinacy.
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FERREIRA, Daniel Furtado. "SISVAR: A COMPUTER ANALYSIS SYSTEM TO FIXED EFFECTS SPLIT PLOT TYPE DESIGNS." REVISTA BRASILEIRA DE BIOMETRIA 37, no. 4 (December 20, 2019): 529. http://dx.doi.org/10.28951/rbb.v37i4.450.
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This paper presents a special capability of Sisvar to deal with fixed effect models with several restriction in the randomization procedure. These restrictions lead to models with fixed treatment effects, but with several random errors. One way do deal with models of this kind is to perform a mixed model analysis, considering only the error effects in the model as random effects and with different covariance structure for the error terms. Another way is to perform a analysis of variance with several error. These kind of analysis, when the data are balanced, can be done by using Sisvar. The software lead a exact $F$ test for the fixed effects and allow the user to applied multiple comparison procedures or regression analysis for the levels of the fixed effect factors, regarding they are single effects, interaction effects or hierarchical effects. Sisvar is an interesting statistical computer system for using in balanced agricultural and industrial data sets.
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Stellingwerf, D. A., and S. Lwin. "Stratified sampling compared with two-phase stratified cluster sampling for timber volume estimation." Netherlands Journal of Agricultural Science 33, no. 2 (May 1, 1985): 151–60. http://dx.doi.org/10.18174/njas.v33i2.16861.
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Two sampling methods were compared on orthophotos (scale 1:10 000) of a rectangular 12 855-ha area of intensively managed Norway spruce forest in Upper Austria. Trees other than spruce >40 yr old were ignored. 'Method 1' used stratified cluster sampling to determine the area proportion of spruce in the total area: 2 clusters of 5 circular 0.05-ha photo plots were randomly selected within each of 8 rectangular sub-blocks in each of 72 blocks into which the area was 'stratified' (systematically divided). Vol. was determined by 2-phase sampling: (i) 2 clusters were randomly selected in each block, and the % crown cover of spruce was determined in each photo plot; (ii) one photo plot was selected in each block (all crown cover classes being represented) for subsequent vol. determination on 72 plots in the field. 'Method 2' used areas of mature spruce on forest management maps, which were copied on to the orthophotos and then divided into 2 nearly equal strata of higher and lower density whose areas were measured by planimeter. Ten field plots were located in each stratum and enumerated in order to determine the s.d. of each stratum and hence the number of additional random field plots required; the total number of field plots was 55. The 2 methods required the same number of man-days (36), method 1 requiring less office work and more field work and computation. If the sampling intensity was reduced, method 1 became less time-consuming than method 2 (except in the field) for the same vol. error. However, method 2 with sampling errors only in vol., is preferred over method 1 which has sampling errors in both area and volume. (Abstract retrieved from CAB Abstracts by CABI’s permission)
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Stewart, R. D., Z. Liu, D. E. Rupp, C. W. Higgins, and J. S. Selker. "A new instrument to measure plot-scale runoff." Geoscientific Instrumentation, Methods and Data Systems 4, no. 1 (March 2, 2015): 57–64. http://dx.doi.org/10.5194/gi-4-57-2015.
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Abstract. Accurate measurement of the amount and timing of surface runoff at multiple scales is needed to understand fundamental hydrological processes. At the plot scale (i.e., length scales on the order of 1–10 m) current methods for direct measurement of runoff either store the water in a collection vessel, which is not conducive to long-term monitoring studies, or utilize expensive installations such as large-scale tipping buckets or flume/weir systems. We developed an alternative low-cost, robust and reliable instrument to measure runoff that we call the "Upwelling Bernoulli Tube" (UBeTube). The UBeTube instrument is a pipe with a slot machined in its side that is installed vertically at the base of a runoff collection system. The flow rate through the slot is inferred by measuring the water height within the pipe. The geometry of the slot can be modified to suit the range of flow rates expected for a given site; we demonstrate a slot geometry that is capable of measuring flow rates across more than 3 orders of magnitude (up to 300 L min−1) while requiring only 30 cm of hydraulic head. System accuracy is dependent on both the geometry of the slot and the accuracy of the water level measurements. Using a pressure sensor with ±7 mm accuracy, the mean theoretical error for the demonstrated slot geometry was ~17% (ranging from errors of more than 50% at low flow rates to less than 2% at high flow rates), while the observed error during validation was 1–25%. A simple correction factor reduced this mean error to 0–14%, and further reductions in error could be achieved through the use of taller, narrower slot dimensions (which requires greater head gradients to drive flow) or through more accurate water level measurements. The UBeTube device has been successfully employed in a long-term rainfall-runoff study, demonstrating the ability of the instrument to measure surface runoff across a range of flows and conditions.
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Stewart, R. D., Z. Liu, D. E. Rupp, C. W. Higgins, and J. S. Selker. "A new instrument to measure plot-scale runoff." Geoscientific Instrumentation, Methods and Data Systems Discussions 4, no. 2 (November 17, 2014): 589–608. http://dx.doi.org/10.5194/gid-4-589-2014.
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Abstract. Accurate measurement of the amount and timing of surface runoff at multiple scales is needed to understand fundamental hydrological processes. At the plot-scale (i.e., length scales on the order of 1 to 10 m) current methods for direct measurement of runoff either store the water in a collection vessel, which is unconducive to long-term monitoring studies, or utilize expensive installations such as large-scale tipping buckets or flume/weir systems. We developed an alternative low-cost, robust and reliable instrument to measure runoff that we call the "Upwelling Bernoulli Tube" (UBeTube). The UBeTube instrument is a pipe with a slot machined in its side that is installed vertically at the base of a runoff collection system. The flow rate through the slot is inferred by measuring the water height within the pipe. The geometry of the slot can be modified to suit the range of flow rates expected for a given site; we demonstrate a slot geometry which is capable of measuring flow rates across more than three orders of magnitude (up to 300 L min−1) while requiring only 30 cm of hydraulic head. System accuracy is dependent on both the geometry of the slot and the accuracy of the water level measurements. With an off-the-shelf pressure transducer sensor, the mean theoretical error for the demonstrated slot geometry was ~17% (ranging from errors of more than 50% at low flow rates to less than 2% at high flow rates), while the observed error during validation was 1–25%. A simple correction factor reduced this mean error to −14%, and further reductions in error could be achieved through the use of taller, narrower slot dimensions (which requires greater head gradients to drive flow) or through more accurate water level measurements. The UBeTube device has been successfully employed in a long-term rainfall-runoff study, demonstrating the ability of the instrument to measure surface runoff across a range of flows and conditions.
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OWUSU-ANSAH, FRANK, and ROBERT N. CURNOW. "PLANNING CLONAL SELECTION PROGRAMMES FOR PERENNIAL CROPS." Experimental Agriculture 54, no. 6 (January 28, 2018): 957–65. http://dx.doi.org/10.1017/s0014479717000618.
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SUMMARYA formula is developed for calculating the expected gain when a first-order autoregressive repeated measures model for the plot errors is assumed. Using examples from our earlier papers, the similarities of the conclusions about the best selection programme from using simulation of an unstructured model and from using the autoregressive formula for expected gain are presented. The autoregressive formula is then used to derive optimal programmes when the number of plots or plot years is fixed for a range of values for the variance of the interactions of clone effects with years relative to the variance of the clone effects and for the variances and covariances between years of the plot residuals. In general, there are advantages in studying many clones at low replication rather than fewer clones at high replication.
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GILL, J. L. "Standard errors for split-split-plot experiments with repeated measurements of animals1." Journal of Animal Breeding and Genetics 105, no. 1-6 (January 12, 1988): 329–36. http://dx.doi.org/10.1111/j.1439-0388.1988.tb00306.x.
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Brown, Shannon E., and Claudia Wagner-Riddle. "Assessment of random errors in multi-plot nitrous oxide flux gradient measurements." Agricultural and Forest Meteorology 242 (August 2017): 10–20. http://dx.doi.org/10.1016/j.agrformet.2017.04.005.
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Lynch, Ann M. "Comparison of fixed-area plot designs for estimating stand characteristics and western spruce budworm damage in southwestern U.S.A. forests." Canadian Journal of Forest Research 33, no. 7 (July 1, 2003): 1245–55. http://dx.doi.org/10.1139/x03-044.
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Various sampling designs were evaluated using data on stand density, stocking, mortality, and top kill, as influenced by the western spruce budworm (Choristoneura occidentalis Freeman) in 17 stands in New Mexico and Colorado. Efficiency improved as plot size decreased from 0.04 to 0.02 ha for all variables and sampling designs, except for 0.01-ha plots, which required extremely large sample sizes and were subject to bias. Cluster designs were much more efficient than simple random sampling designs, allowing twice the reduction in sample size than was gained by relaxing the allowable error from 10 to 15%. Clusters of two plots were as precise as clusters of three plots. Of the four variables evaluated, density required the largest sample sizes, followed by stocking, percent mortality (for stands where mortality exceeded 10%), and top kill. Few plots were necessary to ascertain that mortality was less than 10%. On average, 10 pairs of 0.02-ha plots would estimate density, stocking, and mortality within a 10% allowable error. A field check of density and stocking variables is recommended, and additional samples are suggested in stands with large percent standard errors associated with those variables.
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Nobakht, Morteza, and C. R. R. Clarkson. "A New Analytical Method for Analyzing Linear Flow in Tight/Shale Gas Reservoirs: Constant-Rate Boundary Condition." SPE Reservoir Evaluation & Engineering 15, no. 01 (February 13, 2012): 51–59. http://dx.doi.org/10.2118/143990-pa.
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Summary Hydraulically fractured vertical and horizontal wells completed in shale gas and some tight gas plays are known to exhibit long periods of linear flow. Recently, techniques for analyzing this flow period using (normalized) production data have been put forth, but there are known errors associated with the analysis. In this paper, linear flow from fractured wells completed in tight/shale gas reservoirs subject to a constant-production-rate constraint is studied. We show analytically that the square-root-of-time plot (a plot of rate-normalized pressure vs. square root of time that is commonly used to interpret linear flow) depends on the production rate. We also show that depending on production rate, the square-root-of-time plot may not be a straight line during linear flow; the higher the production rate, the earlier in time the plot deviates from the expected straight line. This deviation creates error in the analysis, especially for flow-regime identification. To address this issue, a new analytical method is developed for analyzing linear-flow data for the constant-gas-rate production constraint. The method is then validated using a number of numerically simulated cases. As expected, on the basis of the analytical derivation, the square-root-of-time plots for these cases depend on gas-production rate and, for some cases, the plot does not appear as a straight line during linear flow. Finally, we found that there is excellent agreement between the fracture half-lengths obtained using this method and the input fracture half-lengths entered in to numerical simulation.
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Denoël, Thibaut, Luca Pedrelli, Giuseppe Pantaleo, and John O. Prior. "A Robust Method for Assaying the Immunoreactive Fraction in Nonequilibrium Systems." Pharmaceuticals 12, no. 4 (December 3, 2019): 177. http://dx.doi.org/10.3390/ph12040177.
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The immunoreactive fraction r provides important information on the functional purity of radiolabeled proteins. It is traditionally determined by saturating the radioimmunoconjugate with an increasing excess of antigen, followed by linear extrapolation to infinite antigen excess in a double inverse “Lindmo plot”. Although several reports have described shortcomings in the Lindmo plot, a systematic examination is lacking. Using an experimental and simulation-based approach, we compared—for accuracy, precision and robustness—the Lindmo plot with the “rectangular hyperbola” extrapolation method based on the Langmuir model. The differences between the theoretical and extrapolated r values demonstrate that nonequilibrium and antigen depletion are important sources of error. The mathematical distortions resulting from the linearization of the data in the Lindmo plot induce fragility towards stochastic errors and make it necessary to exclude low bound fractions. The rectangular hyperbola provides robust and precise r estimates from raw binding data, even for slow kinetics.
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Torresan, Chiara, Ugo Chiavetta, and Jan Hackenberg. "Applying quantitative structure models to plot-based terrestrial laser data to assess dendrometric parameters in dense mixed forests." Forest Systems 27, no. 1 (May 22, 2018): e004. http://dx.doi.org/10.5424/fs/2018271-12658.
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Aim of study: To assess terrestrial laser scanning (TLS) accuracy in estimating biometrical forest parameters at plot-based level in order to replace manual survey for forest inventory purposes.Area of study: Monte Morello, Tuscany region, ItalyMaterial and methods: In 14 plots (10 m radius) in dense Mediterranean mixed conifer forests, diameter at breast height (DBH) and height were measured in Summer 2016. Tree volume was computed using the second Italian National Forest Inventory (INFC II) equations. TLS data were acquired in the same plots and quantitative structure models (QSMs) were applied to TLS data to compute dendrometric parameters. Tree parameters measured in field survey, i.e. DBH, height, and computed volume, were compared to those resulting from TLS data processing. The effect of distance from the plot boundary in the accuracy of DBH, height and volume estimation from TLS data was tested.Main results: TLS-derived DBH showed a good correlation with the traditional forest inventory data (R2=0.98, RRMSE=7.81%), while tree height was less correlated with the traditional forest inventory data (R2=0.60, RRMSE=16.99%). Poor agreement was observed when comparing the volume from TLS data with volume estimated from the INFC II prediction equations.Research highlights: The study demonstrated that the application of QSM to plot-based terrestrial laser data generates errors in plots with high density of coniferous trees. A buffer zone of 5 m would help reduce the error of 35% and 42% respectively in height estimation for all trees and in volume estimation for broadleaved trees.
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Ainsley, A. E., G. V. Dyke, and J. F. Jenkyn. "Inter-plot interference and nearest-neighbour analysis of field experiments." Journal of Agricultural Science 125, no. 1 (August 1995): 1–9. http://dx.doi.org/10.1017/s002185960007444x.
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SUMMARYResults obtained from the plots of a field experiment may be subject to’ representational errors'; that is, they may not exactly reproduce the results that would be expected if the same treatments were applied to whole fields. In particular, the treatment applied to one particular plot often has the potential to affect the adjacent plots; such an effect may be positive or negative according to circumstances, and its magnitude cannot easily be estimated. This paper shows, on theoretical grounds and by simulations, that nearest-neighbour (NN) analysis cannot be relied upon to mitigate the effects of such inter-plot interference, even when the residual variance of the NN analysis is substantially, and by normal criteria significantly, smaller than that of conventional (randomized block) analysis. In certain circumstances, NN analysis of data affected by interference produces greater average bias in estimated treatment differences than conventional analysis.
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SINGH, M., S. CHRISTIANSEN, and B. K. CHAKRABORY. "An assessment of the effect of covariances of plot errors over time on the precision of means of rotations with wheat." Experimental Agriculture 33, no. 4 (October 1997): 469–75. http://dx.doi.org/10.1017/s0014479797004018.
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The introduction of appropriate crop rotations is known to be beneficial in many farming systems. One feature of rotations is that it takes a valuable length of time for the advantage of the rotation to take effect. In long-term rotation trials, the observations from the same plot over years are correlated; ignoring such correlations may affect the precision of the estimates of rotation effects. We examined five covariance structures between the plot errors over time to assess the effect of correlations on the standard errors of rotation means and rotation x cycle combination (interaction) means on wheat yields using eight years of data from six two-phase rotations with wheat. Based on wheat yield data from the four cycles of the rotations considered, the compound symmetry covariance structure (constant correlation) between plot errors arising over alternate years gave more efficient estimates of rotation means compared with the other four covariance structures.
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Son, Keunhong, Sungryul Yu, Wonseok Shin, Kyudong Han, and Keunsoo Kang. "A Simple Guideline to Assess the Characteristics of RNA-Seq Data." BioMed Research International 2018 (November 4, 2018): 1–9. http://dx.doi.org/10.1155/2018/2906292.
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Next-generation sequencing (NGS) techniques have been used to generate various molecular maps including genomes, epigenomes, and transcriptomes. Transcriptomes from a given cell population can be profiled via RNA-seq. However, there is no simple way to assess the characteristics of RNA-seq data systematically. In this study, we provide a simple method that can intuitively evaluate RNA-seq data using two different principal component analysis (PCA) plots. The gene expression PCA plot provides insights into the association between samples, while the transcript integrity number (TIN) score plot provides a quality map of given RNA-seq data. With this approach, we found that RNA-seq datasets deposited in public repositories often contain a few low-quality RNA-seq data that can lead to misinterpretations. The effect of sampling errors for differentially expressed gene (DEG) analysis was evaluated with ten RNA-seq data from invasive ductal carcinoma tissues and three RNA-seq data from adjacent normal tissues taken from a Korean breast cancer patient. The evaluation demonstrated that sampling errors, which select samples that do not represent a given population, can lead to different interpretations when conducting the DEG analysis. Therefore, the proposed approach can be used to avoid sampling errors prior to RNA-seq data analysis.
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Nunes, Thomas L., and Charles C. Goldsmith. "Small, Area X-Ray Diffraction Techniques: Errors in Strain Measurement." Advances in X-ray Analysis 31 (1987): 77–85. http://dx.doi.org/10.1154/s0376030800021868.
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AbstractThe semiconductor industry is continually striving'for smaller, denser devices. Microdiffractometry is an analytical technique which endeavors to apply x-ray diffraction techniques for measuring strain and phase information to areas approaching 50 micrometers diameter or less.This study extends the work of Goldsmith and Walker in the measurement of strain using the lattice distortion or sin2(ψ) plot method.The systematic errors arising from both sample and beam displacement relative to the center of rotation of the cample ie examined. A relationship is derived which predicts the influence of these displacements upon the slope of a stress plot. The predictions are compared to experiment.The influence of random errors arising from particle size and photon counting statistics will also be discussed. Guidelines will be presented which will minimize both systematic and random errors.
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Murgaš, Vlastimil, Ivan Sačkov, Maroš Sedliak, Daniel Tunák, and František Chudý. "Assessing horizontal accuracy of inventory plots in forests with different mix of tree species composition and development stage." Journal of Forest Science 64, No. 11 (December 3, 2018): 478–85. http://dx.doi.org/10.17221/92/2018-jfs.
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Global navigation satellite systems (GNSS) have a wide range of applications in forest industry, including forest inventory. In this study, the horizontal accuracy of 45 inventory plots in different forest environments and 5 inventory plots under open sky conditions were examined. The inventory plots were located using a mapping-grade GNSS receiver during leaf-on season in 2017. True coordinates of the plot centres were acquired using a survey-grade GNSS receiver during leaf-off season in 2018. A study was conducted across a range of forest conditions in the forest unit Vígľaš, which is located in Slovakia (Central Europe). Root mean square error of horizontal accuracies was 8.45 m in the plots under forest canopy and 6.61 m under open sky conditions. We note decreased positional errors in coniferous forests as well as in younger forests. However, results showed that there is no statistically significant effect of tree species composition and stand age on horizontal accuracy.
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Finney, D. J. "Was this in your Statistics Textbook? III. Design and Analysis." Experimental Agriculture 24, no. 4 (October 1988): 421–32. http://dx.doi.org/10.1017/s001447970010016x.
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SummaryThat experimental design is fundamentally more important than the formal analysis of variance of results is emphasized. Attention is drawn to the many weaknesses of split plot designs, and the case for adopting other types of confounding whenever possible is stated. Split plots are sometimes necessary but rarely desirable. The need for randomization is stressed, with a warning against abandoning this principle before fully considering the dangers. The error in analysing repeated observations as though they are from split plots is discussed at length. After brief comment on variance heterogeneity, a final section looks at how measures of the precision of treatment means should be presented. The two main points made are that standard errors of treatment means should always be included in tables of results, and that existing multiple range significance test procedures have little or no practical utility.
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Perren, Andreas, Bernard Cerutti, Mark Kaufmann, and Hans Ulrich Rothen. "A novel method to assess data quality in large medical registries and databases." International Journal for Quality in Health Care 31, no. 7 (January 4, 2019): 1–7. http://dx.doi.org/10.1093/intqhc/mzy249.
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Abstract Background There is no gold standard to assess data quality in large medical registries. Data auditing may be impeded by data protection regulations. Objective To explore the applicability and usefulness of funnel plots as a novel tool for data quality control in critical care registries. Method The Swiss ICU-Registry from all 77 certified adult Swiss ICUs (2014 and 2015) was subjected to quality assessment (completeness/accuracy). For the analysis of accuracy, a list of logical rules and cross-checks was developed. Type and number of errors (true coding errors or implausible data) were calculated for each ICU, along with noticeable error rates (>mean + 3 SD in the variable’s summary measure, or >99.8% CI in the respective funnel-plot). Results We investigated 164 415 patient records with 31 items each (37 items: trauma diagnosis). Data completeness was excellent; trauma was the only incomplete item in 1495 of 9871 records (0.1%, 0.0%–0.6% [median, IQR]). In 15 572 patients records (9.5%), we found 3121 coding errors and 31 265 implausible situations; the latter primarily due to non-specific information on patients’ provenance/diagnosis or supposed incoherence between diagnosis and treatments. Together, the error rate was 7.6% (5.9%–11%; median, IQR). Conclusions The Swiss ICU-Registry is almost complete and data quality seems to be adequate. We propose funnel plots as suitable, easy to implement instrument to assist in quality assurance of such a registry. Based on our analysis, specific feedback to ICUs with special-cause variation is possible and may promote such ICUs to improve the quality of their data.
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Iles, Kim, and Mike Fall. "Can an angle gauge really evaluate "borderline trees" accurately in variable plot sampling?" Canadian Journal of Forest Research 18, no. 6 (June 1, 1988): 776–83. http://dx.doi.org/10.1139/x88-118.
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Detailed measurements on approximately 900 "borderline trees" verified that professional cruisers are precise in their use of wedge prisms, and that errors cancel. The fact that trees are not round gives variable plots that are not round but of the correct size. Noncancelling errors were mostly due to steep slope corrections and difficulty in seeing the tree clearly. The tree count bias was −0.5% of total cruise tree count. Reasons for checking trees are detailed. The authors recommend measuring doubtful trees with a diameter tape, for a variety of reasons, even though the bias is small.
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Smith, E. B., F. M. Williams, and C. R. Fisher. "Effects of intrapopulation variability on von Bertalanffy growth parameter estimates from equal mark–recapture intervals." Canadian Journal of Fisheries and Aquatic Sciences 54, no. 9 (September 1, 1997): 2025–32. http://dx.doi.org/10.1139/f97-110.
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The effects of intrapopulation variability on the parameter estimates of the von Bertalanffy growth equation have received discussion in the literature. Here we evaluated the effects of intrapopulation variability, using computer simulations, on four commonly used methods for estimating the von Bertalanffy growth parameters: the Ford-Walford plot, Ricker's method, Bayley's method, and Fabens' method. Intrapopulation variability in growth rates (k) and maximum sizes ( L infinity ) plus initial size distributions and measurement error, were tested for their effects on the accuracy of the parameter estimates using simulated mark-recapture data with equal recapture intervals. Fabens' method and a modified Ford-Walford plot provided the most accurate estimates in all cases, but when intrapopulation variability was large, they performed poorly. With moderate intrapopulation variability, the bias in estimates was small although between-sample variance was quite large. Biased initial size distributions without either small or large size classes cause a magnification of the estimation errors. Without knowledge of the degree of intrapopulation variability in a natural population, large errors of unknown magnitude in parameter estimation can result, and care should be taken when interpreting these estimates. However, if this variability can be quantified, then approximate parameter estimate errors can be obtained.
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Molto, Q., B. Hérault, J. J. Boreux, M. Daullet, A. Rousteau, and V. Rossi. "Predicting tree heights for biomass estimates in tropical forests." Biogeosciences Discussions 10, no. 5 (May 23, 2013): 8611–35. http://dx.doi.org/10.5194/bgd-10-8611-2013.
Full textAbstract:
Abstract. The recent development of REDD+ mechanisms require reliable estimation of carbon stocks, especially in tropical forests that are particularly threatened by global changes. Even if tree height is a crucial variable to compute the above-ground forest biomass, tree heights are rarely measured in large-scale forest census because it requires consequent extra-effort. Tree height have thus to be predicted thanks to height models. Height and diameter of all trees above 10 cm of diameter were measured in thirty-three half-ha plots and nine one-ha plots throughout the northern French Guiana, an area with substantial climate and environmental gradients. We compared four different model shapes and found that the Michaelis–Menten shape was the most appropriate for the tree biomass prediction. Model parameters values were significantly different from one forest plot to another and neglecting these differences would lead to large errors in biomass estimates. Variables from the forest stand structure explained a sufficient part of the plot-to-plot variations of the height model parameters to affect the AGB predictions. In the forest stands dominated by small trees, the trees were found to have rapid height growth for small diameters. In forest stands dominated by larger trees, the trees were found to have the greatest heights for large diameters. The above-ground biomass estimation uncertainty of the forest plots was reduced by the use of the forest structure-based height model. It demonstrates the feasibility and the importance of height modeling in tropical forest for carbon mapping. Tree height is definitely an important variable for AGB estimations. When the tree heights are not measured in an inventory, they can be predicted with a height-diameter model. This model can account for plot-to plot variations in height-diameter relationship thank to variables describing the plots. The variables describing the stand structure of the plots are efficient for this. We found that variables describing the plot environment (rainfall, topography,...) do not improve the model much.