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Record W4311465370 · doi:10.1002/crso.20240

Agronomic Practices to Minimize Lodging While Maximizing Yield and Protein of Spring Wheat in the Eastern Canadian Prairies

2022· article· en· W4311465370 on OpenAlexaffabout
Amy Mangin, Yvonne Lawley

Bibliographic record

VenueCrops & Soils · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsUniversity of ManitobaManitoba Canola Growers Association
Fundersnot available
KeywordsAgronomyYield (engineering)Spring (device)CultivarCropGrain yieldFertilizerEnvironmental scienceCrop managementGrain qualityMathematicsBiologyEngineering

Abstract

fetched live from OpenAlex

Abstract New high‐yielding spring wheat cultivars are being grown widely across the Canadian Prairies. As yields rise, grain protein levels can only be maintained by increasing the N availability to crop, often reducing fertilizer use efficiency. An additional concern when increasing the amount of N fertilizer being applied to the crop is the increased risk of lodging, reducing the crop’s yield, quality, and harvestability. This article reports on research evaluating the effect of agronomic management practices on spring wheat lodging risk, grain yield, and protein content in the eastern Canadian Prairies. 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 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)0.001
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 score0.250
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.048
GPT teacher head0.233
Teacher spread0.185 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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