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Gotowe bibliografie tematyczne / Hierarchical cluster

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Gotowa bibliografia na temat „Hierarchical cluster”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Hierarchical cluster"

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Elkahlout, Gamal R., and Mohaned A. Elkahlout. "Employing cluster analysis in defining groundwater wells patterns in Rafah." Edelweiss Applied Science and Technology 8, no. 6 (2024): 3542–55. http://dx.doi.org/10.55214/25768484.v8i6.2753.

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Clustering analysis techniques used in identifying homogeneity of groundwater wells patterns in terms of Chloride, Nitrate, and TDS. Data was collected through the laboratories of Palestinian Water Authority in the Rafah 2020. R-Programming is used for data analysis. Kolmogorov-Smirnov test is used to test data normality. Hierarchical clustering analysis applied to generate a cluster tree (r>0.75) in correlation matrix. Agglomerative Hierarchical Clustering is used to classify with “average” method and found that there are two clusters. First cluster with 28 wells and Second cluster with 9

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Huang, Jinke, Xiaoguang Fan, Xin Xiang, Min Wan, Zhenfu Zhuo, and Yongjian Yang. "A Clustering Routing Protocol for Mobile Ad Hoc Networks." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/5395894.

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The dynamic topology of a mobile ad hoc network poses a real challenge in the design of hierarchical routing protocol, which combines proactive with reactive routing protocols and takes advantages of both. And as an essential technique of hierarchical routing protocol, clustering of nodes provides an efficient method of establishing a hierarchical structure in mobile ad hoc networks. In this paper, we designed a novel clustering algorithm and a corresponding hierarchical routing protocol for large-scale mobile ad hoc networks. Each cluster is composed of a cluster head, several cluster gateway

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Godara, Poonam, Shrawan Kumar, and Darvinder Kumar. "Evaluation of Genetic Variation in Indian mustard (Brassica Juncea L Czern and Coss) Using Multivariate Techniques." Journal of Agriculture Research and Technology 47, no. 03 (2022): 344–48. http://dx.doi.org/10.56228/jart.2022.47315.

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A set of 310 lines of Indian mustard (Brassica juncea L Czern and Coss) were analysed for cluster and principal component analysis (PCA). PCA identified four principal components which explained 65.13% of total variability among the 310 genotypes. Hierarchical cluster analysis grouped 310 genotypes into 3 clusters. Cluster1 included maximum number of 155 genotypes and clusters 3 had the lowest number of 43 genotypes. The grouping pattern of genotypes obtained by cluster analysis and PCA plots was almost similar.

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Muhammad Akmal Hafiz Abidin, Satria Raditya Nugroho, and Sri Pingit Wulandari. "Pengelompokan Kabupaten/Kota Berdasarkan Indeks Pembangunan Manusia Provinsi Jawa Timur Tahun 2023." Jurnal Cakrawala Akademika 1, no. 4 (2024): 1143–57. https://doi.org/10.70182/jca.v1i4.10.

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Quality human resources are the key to success in achieving goals, both individual and regional. One of the main indicators of the quality of human resources is the Human Development Index (HDI), which includes poverty, life expectancy, unemployment, education, and labor participation. East Java Province, with its diversity and large population, faces great challenges in improving the quality of human resources. One method of measuring whether these factors affect HDI is by dividing clusters based on the region, then analyzed using cluster analysis. Cluster analysis, both hierarchical and non-

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Avatavului, Cristian, and Costin-Anton Boiangiu. "A Hierarchical Cluster Tree Approach Leveraging Delaunay Triangulation." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 14, no. 3 (2023): 408–33. http://dx.doi.org/10.18662/brain/14.3/482.

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This research introduces a robust and reliable technique for structuring document image pages hierarchically, harnessing the power of Delaunay triangulation. Central to our approach is the formation of a cluster tree, which encapsulates the page's content through strategically exploiting layout elements arrangements and their relative distances. By applying our technique, we proficiently categorize the page into distinct clusters encompassing images, titles, and paragraphs. The consequent hierarchical framework, founded on the cluster tree, establishes a durable and trustworthy blueprint of th

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Schonlau, Matthias. "The Clustergram: A Graph for Visualizing Hierarchical and Nonhierarchical Cluster Analyses." Stata Journal: Promoting communications on statistics and Stata 2, no. 4 (2002): 391–402. http://dx.doi.org/10.1177/1536867x0200200405.

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In hierarchical cluster analysis, dendrograms are used to visualize how clusters are formed. I propose an alternative graph called a “clustergram” to examine how cluster members are assigned to clusters as the number of clusters increases. This graph is useful in exploratory analysis for nonhierarchical clustering algorithms such as k-means and for hierarchical cluster algorithms when the number of observations is large enough to make dendrograms impractical. I present the Stata code and give two examples.

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Bergamini, P., A. Agnello, and G. B. Caminha. "Cluster strong lensing with hierarchical inference." Astronomy & Astrophysics 648 (April 2021): A123. http://dx.doi.org/10.1051/0004-6361/201937138.

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Context. Lensing by galaxy clusters is a versatile probe of cosmology and extragalactic astrophysics, but the accuracy of some of its predictions is limited by the simplified models adopted to reduce the (otherwise intractable) number of degrees of freedom. Aims. We aim to explore cluster lensing models in which the parameters of all cluster member galaxies are free to vary around some common scaling relations with non-zero scatter and deviate significantly from these relations if, and only if, the data require this. Methods. We devised a Bayesian hierarchical inference framework that enables

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Fauziyah, Wardah Muna, and Anneke Iswani Achmad. "Penerapan Analisis Cluster Hybrid untuk Pengelompokan Kabupaten/Kota di Provinsi Jawa Barat Berdasarkan Indikator Kemiskinan Tahun 2022." Bandung Conference Series: Statistics 3, no. 2 (2023): 566–74. http://dx.doi.org/10.29313/bcss.v3i2.8610.

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Abstract. Hybrid cluster analysis is a combination of hierarchical and non-hierarchical clusters, which has a goal as an alternative method. The advantage of hybrid cluster analysis is that it can determine k-clusters for the process of making non-hierarchical clusters through the results of making hierarchical cluster methods, which will produce the right k-clusters. With the advantages of the hybrid cluster analysis, this research will combine the single linkage method with k-means, then the ward method with k-means. The purpose of this study was to determine the grouping of districts/cities

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Ersa Riga Puspita and Mujiati Dwi Kartikasari. "IDENTIFYING THE CLUSTER OF FAMILIES AT RISK OF STUNTING IN YOGYAKARTA USING HIERARCHICAL AND NON-HIERARCHICAL APPROACH." Jurnal Ilmiah Kursor 12, no. 4 (2024): 159–66. https://doi.org/10.21107/kursor.v12i4.358.

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Stunting, or short stature, is a growth disorder usually caused by chronic dietary deficiencies from the prenatal stage to early childhood, typically becoming evident in children after the age of 2. Stunting cases in Yogyakarta Province experienced a decline in 2020. With this development, the government aims to achieve zero stunting in Yogyakarta Province by 2024. To support this goal, a research study was conducted in 2021 to analyze family factors associated with stunting risks in Yogyakarta Province. The study aimed to assist the government in addressing the issue and achieving the target.

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Messakh, Gerald Claudio, Memi Nor Hayati, and Sifriyani Sifriyani. "Penerapan Metode K-Means Dalam Pengelompokan Kabupaten/Kota Di Kalimantan Berdasarkan Indikator Pendidikan." EKSPONENSIAL 14, no. 2 (2023): 57. http://dx.doi.org/10.30872/eksponensial.v14i2.1103.

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Cluster analysis is an analysis that aims to classify data based on the similarity of spesific characteristics. Based on the structure, cluster analysis is divided into two, namely hierarchical and non-hierarchical methods. One of the non-hierarchical methods used in this study is K-Means. K-Means is a partition-based non-hierarchical data grouping method. This purpose of this study is to obtain the best results of grouping regencies/cities on the island of Kalimantan based on education indicators using the K-Means method based on the smallest ratio of standard deviation. Based on the results

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Rozprawy doktorskie na temat "Hierarchical cluster"

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Puch-Solis, Roberto O. "Hierarchical junction trees." Thesis, University of Warwick, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365243.

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Tittley, Eric Robert. "Hierarchical clustering and galaxy cluster scaling laws." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0008/NQ40291.pdf.

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Takahashi, Atsushi. "Hierarchical Cluster Analysis of Dense GNSS Data and Interpretation of Cluster Characteristics." Kyoto University, 2019. http://hdl.handle.net/2433/244510.

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Prokopenko, Sergiy. "Hierarchical binary spatial regression models with cluster effects." [S.l.] : [s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=972223851.

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Kumar, Swapnil. "Comparison of blocking and hierarchical ways to find cluster." Kansas State University, 2017. http://hdl.handle.net/2097/35425.

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Master of Science<br>Department of Computing and Information Sciences<br>William H. Hsu<br>Clustering in data mining is a process of discovering groups in a set of data such that the similarity within the group is maximized and the similarity among the groups is minimized. One way of approaching clustering is to treat it as a blocking problem of minimizing the maximum distance between any two units within the same group. This method is known as Threshold blocking. It works by applying blocking as a graph partition problem. Chameleon is a hierarchical clustering algorithm, that based on d

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Butchart, Kate. "Hierarchical clustering using dynamic self organising neural networks." Thesis, University of Hertfordshire, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338383.

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Annakula, Chandravyas. "Hierarchical and partitioning based hybridized blocking model." Kansas State University, 2017. http://hdl.handle.net/2097/35468.

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Master of Science<br>Department of Computing and Information Sciences<br>William H. Hsu<br>(Higgins, Savje, & Sekhon, 2016) Provides us with a sampling blocking algorithm that enables large and complex experiments to run in polynomial time without sacrificing the precision of estimates on a covariate dataset. The goal of this project is to run the different clustering algorithms on top of clusters formed from above mentioned blocking algorithm and analyze the performance and compatibility of the clustering algorithms. We first start with applying the blocking algorithm on a covariate dataset

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Riley, Owen G. "Termediator-II: Identification of Interdisciplinary Term Ambiguity Through Hierarchical Cluster Analysis." BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/4030.

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Technical disciplines are evolving rapidly leading to changes in their associated vocabularies. Confusion in interdisciplinary communication occurs due to this evolving terminology. Two causes of confusion are multiple definitions (overloaded terms) and synonymous terms. The formal names for these two problems are polysemy and synonymy. Termediator-I, a web application built on top of a collection of glossaries, uses definition count as a measure of term confusion. This tool was an attempt to identify confusing cross-disciplinary terms. As more glossaries were added to the collection, this mea

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Parin, Ylenia <1994&gt. "Revealing Hierarchical Object Processing in Rat’s Visual System Using Cluster Analysis." Master's Degree Thesis, Università Ca' Foscari Venezia, 2018. http://hdl.handle.net/10579/13369.

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Being able of recognizing objects despite changes in their appearance is a necessary skill for the vast majority of living beings. Visual systems, such the one of rats, can solve this task without an apparent effort and with surprising speed. In order to do that, the visual cortex must be able to build a representation of a specific visual stimulus that is invariant to identity-preserving transformations. In particular, the rat lateral extrastriate areas are thought to be organized as a hierarchy, each one representing features of a certain complexity. Several studies have shown that, along th

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Aleksakhin, Vladyslav. "Visualization of gene ontology and cluster analysis results." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-21248.

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The purpose of the thesis is to develop a new visualization method for Gene Ontologiesand hierarchical clustering. These are both important tools in biology andmedicine to study high-throughput data such as transcriptomics and metabolomicsdata. Enrichment of ontology terms in the data is used to identify statistically overrepresentedontology terms, that give insight into relevant biological processes orfunctional modules. Hierarchical clustering is a standard method to analyze andvisualize data to nd relatively homogeneous clusters of experimental data points.Both methods support the analysis

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Książki na temat "Hierarchical cluster"

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Dandamudi, Sivarama. Hierarchical Scheduling in Parallel and Cluster Systems. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0133-6.

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Dandamudi, Sivarama. Hierarchical Scheduling in Parallel and Cluster Systems. Springer US, 2003.

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Padmore, Joanne. An information based measure of dissimilarity for hierarchical cluster analysis. Sheffield University Management School, 1993.

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Nixon, J. How does the UK NHS compare with European standards?: A review of EU health care systems using hierarchical cluster analysis. Centre for Health Economics, University of York, 2000.

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Dandamudi, Sivarama. Hierarchical Scheduling in Parallel and Cluster Systems. Springer, 2003.

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Hierarchical scheduling in parallel and cluster systems. Kluwer Academic/Plenum Publishers, 2003.

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Dandamudi, Sivarama. Hierarchical Scheduling in Parallel and Cluster Systems. Springer, 2012.

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(Editor), Andreas Dress, and A. Von Haeseler (Editor), eds. Trees and Hierarchical Structures: Proceedings of Conference Held at Bielefeld, Frg, Oct. 1987 (Lecture Notes in Biomathematics). Springer, 1990.

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Zang, Shuang-Quan. Atom-Precise Metal Clusters: Surface Engineering and Hierarchical Assembly. Wiley & Sons, Incorporated, John, 2024.

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Zang, Shuang-Quan. Atom-Precise Metal Clusters: Surface Engineering and Hierarchical Assembly. Wiley & Sons, Incorporated, John, 2024.

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Części książek na temat "Hierarchical cluster"

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Ward, Paul A. S., and David J. Taylor. "Self-Organizing Hierarchical Cluster Timestamps." In Euro-Par 2001 Parallel Processing. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44681-8_8.

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Chi, Mingmin, Youdong Miao, Youze Tang, Jón Atli Benediktsson, and Xuanjing Huang. "Hierarchical Ensemble Support Cluster Machine." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_26.

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Focht, Erich, and Andreas Jeutter. "AggMon: Scalable Hierarchical Cluster Monitoring." In Sustained Simulation Performance 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32454-3_5.

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Dandamudi, Sivarama. "Parallel and Cluster Systems." In Hierarchical Scheduling in Parallel and Cluster Systems. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0133-6_2.

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Dandamudi, Sivarama. "Scheduling in Cluster Systems." In Hierarchical Scheduling in Parallel and Cluster Systems. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0133-6_9.

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Dandamudi, Sivarama. "Hierarchical Task Queue Organization." In Hierarchical Scheduling in Parallel and Cluster Systems. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0133-6_4.

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Miyamoto, Sadaaki. "Hierarchical Cluster Analysis and Fuzzy Sets." In Fuzzy Sets in Information Retrieval and Cluster Analysis. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-015-7887-5_6.

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Roux, M. "Basic Procedures in Hierarchical Cluster Analysis." In Eurocourses: Chemical and Environmental Science. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3198-8_4.

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Johs, Hjellbrekke. "MCA and ascending hierarchical cluster analysis." In Multiple Correspondence Analysis For The Social Sciences. Routledge, 2018. http://dx.doi.org/10.4324/9781315516257-5.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Hierarchical Cluster Analysis for Unsupervised Data." In Machine Learning in Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5824-7_15.

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Streszczenia konferencji na temat "Hierarchical cluster"

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Ren, Yongchang, and Lin Zhang. "Hierarchical Cluster Analysis of Network Security." In 2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2024. https://doi.org/10.1109/cicn63059.2024.10847363.

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Williams, Francis J., Samuel L. Hennessey, Ludmila I. Kuncheva, Jose F. Diez-Pastor, and Juan J. Rodriguez. "A Constrained Cluster Ensemble Using Hierarchical Clustering Methods." In 2024 IEEE 12th International Conference on Intelligent Systems (IS). IEEE, 2024. http://dx.doi.org/10.1109/is61756.2024.10705267.

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Joshi, Aditya, Skieler Capezza, and Mo-Yuen Chow. "Strategic Leader Selection and Cluster Formation in Hierarchical Networked Microgrids." In 2024 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10688456.

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Rayudu Tummala, Veera Manikantha, Abhishek Hazra, Alakesh Kalita, and Mohan Gurusamy. "Cluster Based Pseudo Hierarchical Decentralized Federated Learning in UAV Networks." In 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall). IEEE, 2024. https://doi.org/10.1109/vtc2024-fall63153.2024.10757781.

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Hunold, Sascha, and Alexandra Carpen-Amarie. "Hierarchical Clock Synchronization in MPI." In 2018 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2018. http://dx.doi.org/10.1109/cluster.2018.00050.

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Bonilla, Daniel Rubio, Colin W. Glass, Jan Kuper, and Robert de Groote. "Introducing and Exploiting Hierarchical Structural Information." In 2015 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2015. http://dx.doi.org/10.1109/cluster.2015.133.

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Luo, Xi, Wei Wu, George Bosilca, et al. "HAN: a Hierarchical AutotuNed Collective Communication Framework." In 2020 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2020. http://dx.doi.org/10.1109/cluster49012.2020.00013.

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Wang, Teng, Suren Byna, Bin Dong, and Houjun Tang. "UniviStor: Integrated Hierarchical and Distributed Storage for HPC." In 2018 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2018. http://dx.doi.org/10.1109/cluster.2018.00025.

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Dantas, Marco, Diogo Leitao, Claudia Correia, Ricardo Macedo, Weijia Xu, and Joao Paulo. "MONARCH: Hierarchical Storage Management for Deep Learning Frameworks." In 2021 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2021. http://dx.doi.org/10.1109/cluster48925.2021.00097.

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Mitra, Saptashwa, Paahuni Khandelwal, Shrideep Pallickara, and Sangmi Lee Pallickara. "STASH : Fast Hierarchical Aggregation Queries for Effective Visual Spatiotemporal Explorations." In 2019 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2019. http://dx.doi.org/10.1109/cluster.2019.8891029.

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Raporty organizacyjne na temat "Hierarchical cluster"

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Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk, and Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4470.

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Cluster analysis of the efficiency of the recreational forest use of the region by separate components of the recreational forest use potential is provided in the article. The main stages of the cluster analysis of the recreational forest use level based on the predetermined components were determined. Among the agglomerative methods of cluster analysis, intended for grouping and combining the objects of study, it is common to distinguish the three most common types: the hierarchical method or the method of tree clustering; the K-means Clustering Method and the two-step aggregation method. For

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Vacha, Linnet, Jack C. Schryver, Mallikarjun Shankar, and Edmon Begoli. Hierarchical Cluster Analysis of Service Usage and Geographic Variation in Medicare Spending. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1082100.

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Russo, Margherita, Fabrizio Alboni, Jorge Carreto Sanginés, et al. The Changing Shape of the World Automobile Industry: A Multilayer Network Analysis of International Trade in Components and Parts. Institute for New Economic Thinking Working Paper Series, 2022. http://dx.doi.org/10.36687/inetwp173.

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In 2018, after 25 years of the North America Trade Agreement (NAFTA), the United States requested new rules which, among other requirements, increased the regional con-tent in the production of automotive components and parts traded between the three part-ner countries, United States, Canada and Mexico. Signed by all three countries, the new trade agreement, USMCA, is to go into force in 2022. Nonetheless, after the 2020 Presi-dential election, the new treaty's future is under discussion, and its impact on the automo-tive industry is not entirely defined. Another significant shift in this indu

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McPhedran, R., K. Patel, B. Toombs, et al. Food allergen communication in businesses feasibility trial. Food Standards Agency, 2021. http://dx.doi.org/10.46756/sci.fsa.tpf160.

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Background: Clear allergen communication in food business operators (FBOs) has been shown to have a positive impact on customers’ perceptions of businesses (Barnett et al., 2013). However, the precise size and nature of this effect is not known: there is a paucity of quantitative evidence in this area, particularly in the form of randomised controlled trials (RCTs). The Food Standards Agency (FSA), in collaboration with Kantar’s Behavioural Practice, conducted a feasibility trial to investigate whether a randomised cluster trial – involving the proactive communication of allergen information a

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Henderson, Tim, Mincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Chihuahuan Desert Inventory & Monitoring Network. National Park Service, 2021. http://dx.doi.org/10.36967/nrr-2285306.

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A fundamental responsibility of the National Park Service is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and t

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Henderson, Tim, Vincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Northern Colorado Plateau Inventory & Monitoring Network. National Park Service, 2021. http://dx.doi.org/10.36967/nrr-2285337.

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A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources

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Henderson, Tim, Vincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Klamath Inventory & Monitoring Network. National Park Service, 2021. http://dx.doi.org/10.36967/nrr-2286915.

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A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources

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Henderson, Tim, Vincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Mojave Desert Inventory & Monitoring Network. National Park Service, 2021. http://dx.doi.org/10.36967/nrr-2289952.

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A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources

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Henderson, Tim, Vincet Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: North Coast and Cascades Inventory & Monitoring Network. National Park Service, 2022. http://dx.doi.org/10.36967/nrr-2293013.

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A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources

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Henderson, Tim, Vincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Central Alaska Inventory & Monitoring Network. National Park Service, 2022. http://dx.doi.org/10.36967/nrr-2293381.

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A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources

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