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Record W2487279016 · doi:10.2136/sssaspecpub57.2ed.c2

Modeling Soil Organic Carbon Change in Canadian Agroecosystems: Testing the Introductory Carbon Balance Model

2009· book-chapter· en· W2487279016 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSSSA special publication series · 2009
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsAgroecosystemSoil carbonEnvironmental scienceBalance (ability)Carbon fibersEconometricsComputer scienceSoil scienceSoil waterMathematicsEcologyAgricultureAlgorithm

Abstract

fetched live from OpenAlex

This chapter outlines an approach used in Canada to model soil organic carbon (SOC) dynamics. The available empirical data in Canada was assessed, and then a simple linear model was developed. This was followed by assessing a more complex, but still fairly simple, two-pool first-order kinetic model, by comparing estimated changes in SOC to those estimated by a more complex model, conducting stochastic sensitivity analysis, and evaluating the results of model simulations by comparing them to results of long-term experiments in Canada. The chapter provides a summary of research efforts that follows a proposed approach for modeling SOC in Canadian agroecosystems by H. H. Janzen et al. To assess its applicability to Canadian conditions, M. A. Bolinder et al. evaluated the general performance of the Introductory Carbon Balance Model approach using empirical data on rates of SOC change from field experiments.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.207
Teacher spread0.173 · 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