Effects of Ice Cover on Annual Bluegrass and Creeping Bentgrass Putting Greens
Bibliographic record
Abstract
Damage as a result of ice cover on putting greens affects golf courses in cold climates. The objectives of this study were to assess cold‐hardiness levels and injury of annual bluegrass [Poa annua f. reptans (Hausskn.) T. Koyama] and creeping bentgrass (Agrostis stolonifera L. cv. Penncross) under ice cover maintained for various periods of time under laboratory and field conditions. In the lab, cold‐hardened plants of both species were subjected to either snow‐covered, ice‐covered, or ice‐encased treatments and tested for cold‐hardiness levels at various periods of time. Ice‐encased annual bluegrass plants stored for 90 d were dead, while ice‐covered and snow‐covered plants had cold‐hardiness levels of −4°C and −18°C, respectively. In contrast, at 150 days after treatment (DAT), creeping bentgrass that was ice encased had a cold‐hardiness level of −18°C, while snow‐covered plants had a cold‐hardiness level of −27°C. In the field, annual bluegrass and creeping bentgrass plants were subjected to the following treatments: snow cover, ice cover, and snow or ice removed 45 DAT and then were sampled at various times to determine cold‐hardiness levels. As in the lab, ice cover had less impact on bentgrass than annual bluegrass. At 90 DAT, ice‐covered creeping bentgrass had cold‐hardiness levels of −29°C while annual bluegrass plants were dead at 75 DAT. Snow‐covered annual bluegrass plants still had cold‐hardiness levels of −16°C at 75 DAT. Removing the snow or ice after 45 DAT had little or no effect.
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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".