Plant growth regulation and the rebound effect when prohexadione calcium is applied to fairway‐height annual bluegrass and creeping bentgrass swards
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it