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Books on the topic 'Automated Sampling'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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1

Bensema, W. D. Handbook for operation and maintenance of an NBS multisensor automated EM field measurement system. U.S. Dept. of Commerce, National Bureau of Standards, 1986.

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2

Chamberlain, R. The specification of an automated sampling system for the continuous testing of gas meters. UMIST, 1994.

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3

Thrush, Cindy. Automatic stormwater sampling made easy. Water Environment Federation, 1993.

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4

Patterson, A. D. A practical comparison of spot and automatic sampling of water quality. UMIST, 1998.

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5

Geological Survey (U.S.), ed. Comparison of water-quality samples collected by siphon samplers and automatic samplers in Wisconsin. U.S. Dept. of the Interior, U.S. Geological Survey, 2000.

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6

Geological Survey (U.S.), ed. Comparison of water-quality samples collected by siphon samplers and automatic samplers in Wisconsin. U.S. Dept. of the Interior, U.S. Geological Survey, 2000.

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7

Spectral analysis for automated exploration and sample acquisition. National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1992.

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8

Spectral analysis for automated exploration and sample acquisition. National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1992.

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9

Kraberg, Alexandra, Katja Metfies, and Rowena Stern. Sampling, Preservation and Counting of Samples I: Phytoplankton. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199233267.003.0009.

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This chapter reviews phytoplankton sampling and analysis techniques, discussing them in light of their advantages and disadvantages. Different sampling methods have varying levels of precision and accuracy. This means that they affect the ways in which individual data sets can be interpreted, and methods therefore have to be kept consistent within time series to avoid creating artefacts. The discussions cover qualitative and semi-quantitative methods, quantitative methods, sample analysis, automated/semi-automated systems, and molecular methodologies. None of the methods are universally applic
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10

An analysis of the work activities of high school library media specialists in automated and nonautomated library media centers using work sampling. University Microfilms International, 1991.

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11

Comparison of water-quality samples collected by siphon samplers and automatic samplers in Wisconsin. U.S. Dept. of the Interior, U.S. Geological Survey, 2000.

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12

Rish, Irina, Guillermo A. Cecchi, and Aurelie Lozano. Practical Applications of Sparse Modeling. MIT Press, 2014.

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13

Rish, Irina, Guillermo A. Cecchi, and Aurelie Lozano. Practical Applications of Sparse Modeling. MIT Press, 2014.

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14

Rish, Irina, Guillermo A. Cecchi, and Aurelie Lozano. Practical Applications of Sparse Modeling. MIT Press, 2014.

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15

Evaluation of a modified automatic sampler for the collection of water samples for analysis of trace organic compounds or suspended sediment. U.S. Geological Survey, 1991.

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16

Practical Applications of Sparse Modeling. MIT Press, 2014.

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17

Qader, Sarchil, Veronique Lefebvre, Amy Ninneman, et al. A Novel Approach to the Automatic Designation of Predefined Census Enumeration Areas and Population Sampling Frames: A Case Study in Somalia. World Bank, Washington, DC, 2019. http://dx.doi.org/10.1596/1813-9450-8972.

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18

Essick, John. The Python-Based Laboratory. Oxford University PressOxford, 2024. https://doi.org/10.1093/9780191998478.001.0001.

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Abstract The Python-Based Laboratory: A Hands-On Guide for Scientists and Engineers provides a learn-by-doing approach to acquiring the Python programming skills needed to implement computer-controlled experimental work. The Python-Based Laboratory leads its readers to mastery of the popular, open-source Python computer language in its role as a powerful laboratory tool by carrying out interesting and relevant projects that explore the acquisition, production, analysis, and presentation of digitized waveforms. Readers, who are assumed to have no prior computer programming or Python background,
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