Variability for Freezing Tolerance among 42 Ecotypes of Green‐Type Annual Bluegrass
Bibliographic record
Abstract
ABSTRACT Limited information exists on the extent of genetic variability for freezing tolerance among perennial biotypes of annual bluegrass (Poa annua L.) that evolved under golf greens management. We characterized the freezing tolerance of 42 ecotypes collected across the United States and in Québec using plants hardened to low temperatures during fall and winter. We subsequently analyzed cold‐induced biochemical changes in a subset of ecotypes with varied levels of tolerance. There was a large variability among ecotypes for freezing tolerance expressed as the lethal temperature for 50% of the plants (LT50) with values ranging from <−27.0 to −17.0°C. Variation was observed between ecotypes originating from the same region and even the same golf course. Ecotypes from Québec—better insulated from extreme subfreezing temperatures by reliable and abundant snow cover—developed less freezing tolerance than those evolving under milder winter climates. Significant differences in the concentrations of specific amino acids and carbohydrates were observed among ecotypes. Only fructans of high molecular weight, however, were significantly correlated with freezing tolerance and accounted for as much as 50% of the LT50 variance. A 26‐kDa polypeptide that markedly accumulated in cold acclimated crowns was more abundant in plants from Québec. Extensive genetic variability for freezing tolerance among perennial biotypes of annual bluegrass can thus be exploited to mitigate winter damage to golf greens.
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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 | 0.001 | 0.001 |
| 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".