The relationships between dehydrins, cold hardiness, and the ambient temperature: comparing a cold hardy to a cold tender <i>Vitis vinifera</i> L. cultivar
Bibliographic record
Abstract
Cold hardiness is a complex multi-genic trait that is influenced by ambient temperature and several metabolites. Dehydrins proteins were recently found to be positively correlated to cold hardiness in the cold tender Vitis vinifera L . cv. Sauvignon blanc. The goals of this study were to test the relationships between dehydrins and cold hardiness in a more cold hardy V. vinifera L. cv., Riesling, and to determine how the relationships between ambient temperature, cold hardiness, and dehydrins differ between genotype of contrasting hardiness phenotype. Dormant buds were sampled biweekly from a commercial vineyard with several clone and rootstock combinations of Riesling and Sauvignon blanc to quantify cold hardiness by differential thermal analysis and dehydrin content by semiquantitative immunoblotting. Multiple linear regressions were used to evaluate the importance of clone, rootstock, and cultivar in the relationships between temperature preceding sampling, cold hardiness, and dehydrin content. We report that in Riesling, dehydrin band intensity at 35, 41, 48, and 90 kDa is correlated to cold hardiness. Temperature preceding sampling explained a much higher portion of the change in hardiness than the change in dehydrin, and interactions between the cultivar and the temperature were calculated for cold hardiness and the bands at 26 and 35 kDa. Clone and rootstock genotypes only had minor influence on the various relationships tested. Taken together, our results suggest that dehydrins play an indirect role in the cold hardiness response of V. vinifera.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".