MétaCan
Menu
Back to cohort
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 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.001

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 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

Citations13
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueCrops & SoilsSame topicCrop Yield and Soil FertilityFrench-language works237,207