A re-evaluation of controlled freeze-tests and controlled environment hardening conditions to estimate the winter survival potential of hardy winter wheats
Bibliographic record
Abstract
To identify superior winter-hardy winter wheat genotypes it is essential to have a reliable screening method that can detect small differences in freezing tolerance. A highly significant correlation was obtained between the minimum temperature tolerated by fully cold-hardened seedlings and the field survival index for 36 winter wheat cultivars with freezing tolerance varying from –13°C to –23°C. On the basis of their long-term field survival under cold stress, these cultivars represent two separate genotypic groups, semi-cold-hardy (Group A) and very cold-hardy (Group B). The correlation coefficient between minimum survival temperature and winter survival for the semi-hardy genotypes was not significant, although it was significant for the hardy genotypes (coefficient of determination was 25.9%). However, the minimum survival freeze test did not differentiate genotypes that varied widely in field survival. In comparing the very hardy winter genotypes (e.g., Norstar, Alabaskaja, Roughrider, etc.), no significant correlation was observed between either minimum survival temperature or crown moisture content. The freezing tolerance of 33 winter wheat genotypes was compared for seedlings naturally cold acclimated and for seedlings grown either in soil or hydroponically and hardened in a controlled environment chamber. On average, soil-grown seedlings, cold acclimated in a controlled environment were more freezing tolerant than seedlings acclimated naturally or grown hydroponically and acclimated in a controlled environment. Several semi-winter-hardy genotypes attained a freezing tolerance equivalent to that of very hardy winter genotypes when acclimated in a controlled environment chamber. Thus, it is possible to overestimate the freezing tolerance of seedlings acclimated in a controlled environment. Key words: Winter wheat, freezing tolerance, winter injury, screening techniques
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".