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Record W4376255494 · doi:10.1002/crso.20277

Comparison of Integrated Crop Rotation Systems in Western Canada

2023· article· en· W4376255494 on OpenAlexaffabout
Sheri Strydhorst, Kui Liu

Bibliographic record

VenueCrops & Soils · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCropCrop rotationYield (engineering)Crop managementAgricultural economicsCrop yieldGeographyProduction (economics)AgroforestryAgricultural scienceAgricultural engineeringCrop productionEnvironmental scienceAgronomyAgricultureEconomicsEngineeringForestryBiology

Abstract

fetched live from OpenAlex

Abstract Research has shown the benefits of diversifying crop rotations, yet many Prairie farmers keep their rotations short and simple with cereals and oilseeds or cereals and pulses being intensively grown in two‐year rotations. As western Canadian farms are pressed to increase yields while reducing inputs and the environmental impact of food production, growers need help to determine what crop rotations can help them achieve these goals and remain economically viable. This article reports on research evaluating yield and yield stability, nitrogen use efficiency, and net economic returns of six crop rotations in the Southern Prairies, Northern Prairies, and Red River Valley ecozones of western Canada. Earn 0.5 CEUs in Crop Management by reading this article and taking the quiz at https://web.sciencesocieties.org/Learning‐Center/Courses .

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)0.001
metaresearch head score (Gemma)0.002
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 score0.032
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.267
Teacher spread0.232 · 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
Published2023
Admission routes2
Has abstractyes

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