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Record W3004264203 · doi:10.1002/crso.20001

Crop Production and Environmental Impacts under Organic Management with Reduced Tillage and Diversified Cropping

2020· article· en· W3004264203 on OpenAlexaffabout
M. R. Fernandez, R.P. Zentner, Michael P. Schellenberg, Olanike Aladenola, Julia Y. Leeson, Mervin St. Luce, B.G. McConkey, H. Cutforth

Bibliographic record

VenueCrops & Soils · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsTillageGreen manureCrop rotationCroppingAgroforestryOrganic farmingAgronomyWeed controlEnvironmental scienceMulch-tillSoil managementAgricultureNo-till farmingSoil fertilityNutrient managementManureCropGeographyBiologySoil water

Abstract

fetched live from OpenAlex

In the Canadian Prairies, organic agriculture has traditionally relied on summer fallow and mechanical tillage for nutrient and pest management. More recently, there has been a substantial increase in the use of legume green manure, diversified crop rotations, and reduced tillage. The objectives of this study were to determine if diversified crop rotations and reduced tillage under organic management can maintain soil fertility and quality at adequate levels, keep weed populations at low levels, and foster healthy plants for sustainable and profitable production of annual crops. Earn 1.5 CEUs in Crop Management by reading this article and taking the quiz at https://www.certifiedcropadviser.org/education/classroom/classes/692 .

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 score0.290
Threshold uncertainty score0.576

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.183
Teacher spread0.168 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

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