Bibliographic record
Abstract
Most strawberry cultivars have flowers that are sensitive to temperatures below 0°C. The development of early or very early cultivars with frost resistant flowers is essential in climates with a danger of spring frosts. Traditionally, breeding programs have used visual screening methods to evaluate the damage to pistils and anthers caused by frost. These methods rely on natural seasonal conditions, are time consuming, and do not provide accurate information on the exact temperature that caused the damage. The objective of this study was to evaluate the use of chlorophyll fluorescence (CF) to estimate the low temperature susceptibility of 64 strawberry cultivars. Strawberry flowers were exposed to continuous low temperatures (0°C for 24 h, 1°C for 24 h, -2°C for 24 h, and finally -3°C for 24 h) and CF was measured following the treatments. Variable fluorescence (Fv) decreased somewhat with time in all genotypes when the flowers were held at -3°C, however, the reduction varied with cultivar. The slight reduction of Fv in the more chilling-tolerant cultivars was not significant, while significant linear or quadratic declines were observed in the more chilling susceptible cultivars. Overall, chlorophyll fluorescence appears to be an effective, simple method for evaluating the low temperature susceptibility of strawberry genotypes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".