Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.004 | 0.001 |
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 it