Plant growth regulation and the rebound effect when prohexadione calcium is applied to fairway‐height annual bluegrass and creeping bentgrass swards
Bibliographic record
Abstract
Abstract Plant growth regulators (PGRs) are commonly used to manage turfgrass growth on golf courses. Growing degree day (GDD) models predict the need for reapplication of PGRs, such as trinexapac‐ethyl (TE) resulting in a potential loss of regulation. Optimal GDD models for application of prohexadione calcium (PC), a late‐stage gibberellin inhibitor, on fairway‐height turfgrasses are currently unknown. The effect of PC and TE on plant growth and stand health were evaluated in two separate seasons on mixed stands of creeping bentgrass (Agrostis stolonifera L.) and annual bluegrass (Poa annua L.) maintained at 9‐mm height at the Guelph Turfgrass Institute. Five treatments (control, PC 2.8 g 100 m−2 [0.09 oz 1000 ft−2], PC 5.6 g 100 m−2 [0.18 oz 1000 ft−2], PC 8.4 g 100 m−2 [0.27 oz 1000 ft−2], and TE 8.0 mL 100 m−2 [0.26 fl oz 1000 ft−2]) were applied based on a label rate GDD schedule. Plant clipping dry weight (DW), visual color ratings and normalized difference vegetative index (NDVI) were assessed. Most PC and TE treatments effectively reduced DW and had a positive effect on visual color and NDVI. A relationship was observed between PC application rates, suggesting that higher application rates allow for greater regulation of plant growth. Rebound effects or periods of excess growth, occurred when reapplication intervals exceeded 350 GDD and had an average of thermal time greater than 21.0 GDD over a 10‐day period. Using optimal GDD models for PC will assist in the effective regulation of turfgrass growth and improved stand health.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".