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Record W296238760 · doi:10.1201/9780203739273-5

Soil Carbon Dynamics in Canadian Agroecosystems

2018· book-chapter· en· W296238760 on OpenAlexaboutno aff
H. H. Janzen, C. A. Campbell, E. G. Gregorich, B. H. Ellert

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAgroecosystemEnvironmental scienceSoil carbonAgroforestryGeographyForestrySoil scienceSoil waterAgricultureArchaeology

Abstract

fetched live from OpenAlex

The chapter seeks to review changes in soil organic carbon (SOC) in Canadian agroecosystems, with particular emphasis on the effects of various management options. It aims to determine the trajectory of soil C dynamics, describe some of the rate-determining mechanisms, and estimate the potential of various agroecosystems for removing atmospheric carbon dioxide (CO 2 ). The emission of CO 2 from external energy expenditure also merits inclusion in the overall C cycle of a given agroecosystem. Projected changes whose influence on future SOC dynamics also deserve attention include changes to climate, atmospheric CO 2 concentration, ultraviolet-B intensity, and N deposition patterns. Historically, Canadian agricultural soils have been a significant source of atmospheric CO 2 . Approximately 25% of the SOC originally present in the surface layer was lost to the atmosphere upon conversion to arable agriculture. Most agriculture soils in Canada have been cultivated for a sufficient duration that any lingering effects of the initial cultivation are probably overshadowed by the impact of management options.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.012
GPT teacher head0.187
Teacher spread0.175 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations106
Published2018
Admission routes1
Has abstractyes

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