007 Evaluation of the Spring Frost Susceptibility of Strawberry Genotypes Using Chlorophyll Fluorescence Measurements
Bibliographic record
Abstract
Frost tolerance of flower buds is one of the most important characteristics of strawberry cultivars that produce fruit early or very early in the season. The objective of this study was to evaluate chlorophyll fluorescence (CF) as a suitable rapid method to assess spring frost injury of strawberry flowers. More specifically, to determine if there was a relationship between a decrease in CF and the appearance of visual symptoms (visual expression of necrosis (VEN) based on the amount of dark, damaged, and/or water soaked tissue of the pistil) due to frost. Sixty-six strawberry genotypes with varying levels of chilling susceptibility were used. The plants were grown in a greenhouse under a 16-h light period at 20-22 °C during the daytime and 16-18 °C at night. For the CF and VEN measurements, the plants were stored at -3 °C for 24 h followed by 24 h in the greenhouse. The CF measurements were made on dark-adapted tissue, using the F v /F m test of an OS-500 modulated fluorometer. For the VEN method, the flowers which had dark, damaged, and/or water soaked pistils were counted. The results showed that variable fluorescence (F v ) decreased as the temperature was lowered. The spring frost resistant cultivars maintained F v at a stable level and had a smaller regression slope (ß 1 ), whereas the susceptible cultivars showed a very dramatic decrease in F v . The CF method gave results that correlated with the VEN results. The strong relationship between chilling tolerance determined via visual and florescence techniques supports the use of CF in selecting resistant spring frost selections in a breeding program. The use of CF will allow the breeder not only to select for spring frost-resistant selections independently of environmental changes, but also to select frost resistant seedlings prior to planting in the field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".