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The net flux of carbon from agricultural soils in Canada 1970–2010

2000· article· en· W2077030153 on OpenAlexaffabout
Ward Smith, R. L. Desjardins, Elizabeth Pattey

Bibliographic record

VenueGlobal Change Biology · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsEnvironmental scienceSoil waterTillageSoil carbonGreenhouse gasAgricultureCarbon sinkSink (geography)Agricultural landClimate changeSoil qualityFertilizerLand use, land-use change and forestryHydrology (agriculture)AgronomySoil scienceGeographyEcologyGeology

Abstract

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Summary Changes in soil organic carbon (SOC) in agricultural soils influence soil quality and greenhouse gas concentrations in the atmosphere. Land use, management practices, soil characteristics, and climate influence such changes. Using the Century model we estimated the rate of SOC change in agricultural soils of Canada for the period 1970 to 2010. This estimation was based on the estimated SOC change for 15% of the 1250 agriculturally designated soil landscape of Canada (SLC) polygons. Simulations were carried out for two to five crop rotations and for conventional and no‐tillage. The results indicate that the agricultural soils in Canada, whose SOC are currently very close to equilibrium, will stop being a net source of CO 2 and will become a sink by the year 2000. Rates of carbon change for the years 1970, 1990, and 2010 were estimated to be −67, − 39, and 11 kgC ha −1 . The rate of decline in the carbon content of agricultural soils in Canada has slowed considerably in the 1990s as a result of an increase in the adoption of no‐tillage management, a reduction in the use of summer fallowing, and an increase in fertilizer application. We estimate that the proportion of agricultural land storing SOC will have increased from 17% in 1990 to 53% by the year 2000.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.257

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 science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.205
Teacher spread0.186 · 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 teacher head, 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

Citations117
Published2000
Admission routes2
Has abstractyes

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