Recovery of Bermudagrass Varieties from Divot Injury
Bibliographic record
Abstract
Intensively used turf areas in the southern United States are commonly established to bermudagrass (Cynodon dactylon (L.) Pers.C. dactylon × C. transvaalensis Burtt‐Davy), partly due to its good recuperative potential. However, little scientific data is available regarding recuperative differences among bermudagrass varieties. The objective of the following research was to quantify differences in injury recovery among the forty‐eight bermudagrass entries in the 2002 National Bermudagrass Test of the National Turfgrass Evaluation Program (NTEP). The trial was maintained under typical golf course fairway conditions and divot injury was simulated in 2003 and 2004. A digital image was collected of each divot on the day of injury and regularly thereafter until full recovery was reached. Divot images were analyzed for percent green turf cover using digital image analysis to quantify recovery percentages. Although divots recovered more quickly in 2004 than in 2003, differences among varieties remained relatively consistent across years. On average, seeded varieties reached 50% recovery one day faster than vegetatively propagated varieties. Among commercially available varieties, ‘La Paloma’ and ‘Yukon’ were fastest to recover while ‘Tifsport’ and ‘Ashmore’ were among the slowest to recover when averaged across years.
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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) | 0.001 | 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".