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Record W639255468

Assessment methods for soil carbon

2001· book· en· W639255468 on OpenAlexaboutno aff
Roshan Lal

Bibliographic record

VenueLewis Publishers eBooks · 2001
Typebook
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSoil carbonTotal organic carbonSoil waterOrganic matterSoil testSoil organic matterEnvironmental scienceGrossmanSoil surveyCarbon fibersEnvironmental chemistryBiomass (ecology)Sampling (signal processing)ForestrySoil scienceChemistryMathematicsAgronomyEcologyGeographyBiology
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION Methods for Assessing Soil C Pools, J.M. Kimble, R. Lal, and R.F. Follett SOIL SAMPLING AND SAMPLE PREPARATION Methodology for Sampling and Preparation for Soil Carbon Determination, J. Kimble, R.B. Grossman, and S. Samson-Liebig Importance of Soil Bulk Density and Methods of its Measurement, R. Lal and J. Kimble The Effects of Terrain Position and Elevation on Soil C in the Southern Appalachians, P.V. Bolstad and J.M. Vose Approaching Functional Soil Organic Matter Pools Through Particle-Size Fractionation: Examples for Tropical Soils, C. Feller, J. Balesdent, B. Nicolardot and C. Cerri Spatial Variability: Enhancing the Mean Estimate of Organic and Inorganic Carbon in a Sampling Unit, L.P. Wilding, L.R. Drees, and L.C. Nordt Assessment of Soil Organic Carbon Using the U.S. Soil Survey, R.B. Grossman, D.S. Harms, D.F. Kingsbury, R.K. Shaw, and A.B. Jenkins Organic Carbon Methods, Microbial Biomass, Root Biomass, and Sampling Design Under Development by NRCS, C.D. Franks, J.M. Kimble, S.E. Samson-Liebig, and T.M. Sobecki ASSESSMENT OF CARBON POOLS Characterization of Soil Organic Carbon Pools, H.H. Cheng, and J. Kimble Measuring and Comparing Soil Carbon Storage, B.H. Ellert, H.H. Janzen, and B.G. McConkey Estimating Total System C in Small Hold and Farming Systems of the East African Highlands, P. Woomer, N.K. Karanja, and E.W. Murage Assessment and Significance of Labile Organic C Pools in Forest Soils, P.K. Khanna, B. Ludwig, J. Bauhus, and C. O'Hara ASSESSMENT OF ANALYTICAL TECHNIQUES Interlaboratory Carbon Isotope Measurement on Five Soils, R.F. Follett and E.G. Pruessner The Determination of Soil C Pool Sizes and Turnover Rates: Biophysical Fractionation and Tracers, E.A. Paul, S.J. Morris, and S. Bohm Ecozone and Soil Profile Screening for C-Residence Time, Rejuvenation, Bomb 14C Photosyntheticd13C Changes, H.W. Scharpenseel, E.M. Pfeiffer, and P. Becker-Heidmann Use of 13C Isotopes to Determine Net Carbon Sequestration in Soil Under Ambient and Elevated CO2, W.R. Horwath, C. Van Kessel, U. Hartwig, and D. Harris Methods Using Amino Sugars as Markers for Microbial Residues in Soil, W. Amelung Characterization of Soil Organic Matter, C.L. Ping, G.J. Michaelson, X.Y. Dai, and R.J. Candler Fractioning Soil in Stable Aggregates Using a Rainfall Simulator, G.C. Starr, R. Lal, and J. Kimble Towards an Efficient Method for Measuring Total Organic Carbon Stocks in Forests, G.R. Smith Soil Organic Matter Elevation, R.A. Rossell, J.C. Gasparoni, and J.A. Galantini The Development of the KMnO4 Oxidation Technique to Determine Labile Carbon in Soil and Its Use in a Carbon Index Management, G. Blair, R. Lefroy, A. Whitbread, N. Blair, and A. Conteh Effects of Soil Morphological and Physical Properties on Estimation of Carbon Storage in Arctic Soils, G.J. Michaelson, C.L. Ping, and J.M. Kimble Estimation of Particulate and Total Organic Matter by Weight-Loss-on-Ignition, C.A. Camberdella, A.M. Gajda, J.W. Doran, B.J. Wienhold, and T.A. Kettler Use of Near Infrared Spectroscopy to Determine Inorganic and Organic Carbon Fractions in Soil and Litter, B. Ludwig and P.K. Khanna Development of Rapid Instrumental Methods for Measuring Soil Organic Carbon, G.W. McCarty and J.B. Reeves, III Soil Quality Evaluations of Alternative and Conventional Management Systems in the Great Plains, A.M. Gajda, J.W. Doran, T.A. Kettler, B.J. Wienhold, J.L. Pikul, Jr., and C.A. Cambardella SOIL EROSION AND SEDIMENTATION 137Cs for Measuring Soil Erosion and Redeposition: Application for Understanding Soil Carbon, J.C. Ritchie Assessing the Impact of Erosion on Soil Organic Carbon Pools and Fluxes, G.C. Starr, R. Lal, and J. Kimble, and L. Owens Assessing Water Erosion Impacts on Soil Carbon Pools and Fluxes, P.A. Jacinthe, R. Lal, and J. Kimble Soil Organic Carbon Erosion Assessment by Cesium-137, Y. Hao, R. Lal, L.B. Owens, and R. C. Izaurralde MODELING AND SCALING PROCEDURES A Simple Model to Estimate Soil Carbon Dynamics at the BOREAS Northern Study Area, Manitoba, Canada, G. Rapalee Methods Used to Create the North American Soil Organic Carbon Digital Database, R. Lacelle, S. Waltman, N. Bliss, and F. Orozco-Chavez Basic Principles for Soil Carbon Sequestration and Calculating Dynamic Country-Level Balances Including Future Scenarios, O. Andren and T. Katterer Examining the Carbon Stocks of Boreal Forest Ecosystems at Stand and Regional Scales, J.S. Bhatti, M.J. Apps, and H. Jiang Predicting Broadscale C Stores of Woody Detritus from Plot Data, M.E. Harmon, O.N. Krankina, M. Yatskov, and E. Matthews Soil C Dynamics: Measurement, Simulation, and Site-to-Region Scale-Up, R.C. Izaurralde, K.H. Haugen-Kozyra, D.C. Jans, W.B. McGill, R.F. Grant, and J.C. Hiley Some Factors Affecting the Distributions of Carbon in Soils of a Dryland Agricultural System in Southwestern Australia, R.J. Harper and R.J. Gilkes A National Inventory of Changes in Soil Carbon from National Resources Inventory Data, M.D. Eve, K. Paustian, R. Follett, and E.T. Elliot ECONOMICS AND POLICY ISSUES Assessing the Economics of Carbon Sequestration in Agriculture, L. Tweeten, B. Sohngen, and J. Hopkins Climate Change Policy and the Agricultural Sector, D. Zilberman and D. Sunding Approaches to Assessing Carbon Credits and Identifying Trading Mechanisms, A. Manale SYNTHESIS Methodological Challenges Toward Balancing Soil C Pools and Fluxes, R. Lal, J.M. Kimble, and R.F. Follett

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.041
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0050.005
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0410.016

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.294
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2001
Admission routes1
Has abstractyes

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