Plant growth regulator effects on red fescue seed crops in diverse production environments
Bibliographic record
Abstract
Abstract Strong creeping red fescue (Festuca rubra L. spp. rubra Gaudin) is a cool‐season perennial turfgrass widely used in temperate and subalpine regions around the globe. Although creeping red fescue turf is tolerant of shade, low fertility acidic soils, and drought conditions, creeping red fescue seed crops grown in optimal growing environments can lodge, ultimately reducing yield in regions where this important turfgrass is grown for seed. To address this issue, we investigated the effects of two plant growth regulators (PGRs), chlormequat chloride (CCC) and trinexapac‐ethyl (TE), on plant height, lodging, and seed yield of strong creeping red fescue over 9 site‐years in the Peace River region of western Canada. The study encompassed 6 site‐years with first‐year stands and 3 site‐years with second‐year stands. The PGRs were applied alone and in a TE + CCC mixture at the two‐node (BBCH 32–33, where BBCH is Biologische Bundesanstalt, Bundessortenamt and Chemische Industrie) and early head emergence (BBCH 51–52) growth stages in first‐ and second‐year stands, respectively. The application of TE, CCC, and their mixture resulted in a differential decrease in lodging and an increase in seed yield in first‐year stands. However, PGRs applied at BBCH 51–52 on second‐year stands had no effect on seed yield but reduced plant height and lodging. This study found a negative correlation between seed yield and lodging. Among the PGR treatments, the CCC + TE mixture was the most effective in reducing lodging and increasing seed yield of strong creeping red fescue.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".