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Record W2917578541 · doi:10.1139/cjss-2018-0146

Soil health indicators after 21 yr of no-tillage in south coastal British Columbia

2019· article· en· W2917578541 on OpenAlexafffundvenueabout
Ben W. Thomas, Derek Hunt, Shabtai Bittman, Kirsten D. Hannam, Aimé J. Messiga, Dennis Haak, Mehdi Sharifi, Xiying Hao

Bibliographic record

VenueCanadian Journal of Soil Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsTillageEnvironmental scienceSoil healthAgronomyAgricultureNo-till farmingConventional tillageSoil waterGeographySoil organic matterSoil fertilitySoil scienceBiology

Abstract

fetched live from OpenAlex

The lower Fraser Valley is one of the most intensively cropped regions in Canada. Yet, how soil health indicators respond to long-term intensive agricultural management is poorly documented in this region. Thus, we evaluated a suite of soil health indicators in response to 21 growing seasons of continuous silage corn (Zea mays L.) under conventional tillage or no-tillage (0–20 cm soil layer). Wet aggregate stability, available water capacity, active carbon (permanganate oxidizable, POXC), and extractable potassium and extractable magnesium were significantly greater with no-till than conventional tillage, whereas 8 of 13 indicators were similar. Soil health indicators responded more favourably to no-till than conventional tillage.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.186
Teacher spread0.179 · 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

Citations14
Published2019
Admission routes4
Has abstractyes

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