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Record W7091399845 · doi:10.1002/csc2.70179

Plant growth regulator effects on seed yield of timothy

2025· article· en· W7091399845 on OpenAlexaffabout

Bibliographic record

VenueCrop Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsPeace Arch HospitalAgriculture and Agri-Food Canada
Fundersnot available
KeywordsPerennial plantForageYield (engineering)Plant growthChlormequatCropTemperate climateFodder

Abstract

fetched live from OpenAlex

Abstract Timothy ( Phleum pratense L.) is a cool‐season perennial forage grass species widely utilized for animal feed and fodder in temperate regions of the world. Timothy is one of the main forage grass crops grown for seed in the Peace Region of western Canada. Timothy performs well under well‐fertilized and high‐moisture soils, but seed production fields are prone to lodging under such conditions. Lodging in grass crops reduces seed production through self‐shading, which limits successful pollination, fertilization, and seed fill. In this study, we investigated the effects of two plant growth regulators (PGRs), chlormequat chloride (CCC) and trinexapac‐ethyl (TE), on plant height, lodging, seed weight, and seed yield of timothy for 8 site‐years. The study encompassed 5 years at one site with crop stands in their first to fifth year of seed production and 3 years at a second site with crop stands in their first to third year of seed production. The PGRs were applied alone (TE and CCC) and in a mix (TE + CCC) at the 2–3 node (Biologische Bundesanstalt, Bundessortenamt, and Chemische Industrie [BBCH] 32–33) and early heading (BBCH 51–52) growth stages. The application of PGRs (TE, CCC, and their mix) at two different growth stages showed a differential decrease in lodging and plant height and an increase in seed yield in all but 1 site year. Among the PGR treatments, CCC applied at the BBCH 32–33 was the most effective in increasing seed yield and economic returns of timothy.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.146

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.229
Teacher spread0.214 · 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 designBench or experimental
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

Citations1
Published2025
Admission routes2
Has abstractyes

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