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Record W2903013327 · doi:10.2134/cs2018.51.0608

Plant growth regulators: What agronomists need to know

2018· article· en· W2903013327 on OpenAlexaffabout
Sheri Strydhorst, Linda M. Hall, L. A. Perrott

Bibliographic record

VenueCrops & Soils · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsUniversity of AlbertaAlberta Ministry of Agriculture and Forestry
Fundersnot available
KeywordsYield (engineering)AgronomyProduction (economics)Plant growthConstraint (computer-aided design)Grain yieldCrop productionPlant productionWork (physics)CropAgricultural engineeringReading (process)AgroforestryBiologyBusinessAgricultureMathematicsPolitical scienceEconomicsEngineeringEcology

Abstract

fetched live from OpenAlex

In western Canadian cereal crops, lodging is still a major production constraint in high‐yield environments, and growers are looking for agronomic solutions. Plant growth regulators are synthetic compounds can work to produce shorter stems, reduce lodging, and maintain grain yield. Earn 1 CEU in Crop Management by reading this article and taking the quiz at www.certifiedcropadviser.org/education/classroom/classes/613 .

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.011
Open science0.0020.001
Research integrity0.0090.009
Insufficient payload (model declined to judge)0.0160.010

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 head0.015
GPT teacher head0.219
Teacher spread0.205 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2018
Admission routes2
Has abstractyes

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