Molecular physiology and breeding at the crossroads of cold hardiness improvement
Bibliographic record
Abstract
Alfalfa (Medicago sativa L.) is a major forage legume grown extensively worldwide with important agronomic and environmental attributes. Insufficient cold hardiness is a major impediment to its reliable production in northern climates. Improvement of freezing tolerance using conventional breeding approaches is slowed by the quantitative nature of inheritance and strong interactions with the environment. The development of gene-based markers would facilitate the identification of genotypes with superior stress tolerance. Successive cycles of recurrent selection were applied using an indoor screening method to develop populations with significantly higher tolerance to freezing (TF). Bulk segregant analysis of heterogeneous TF populations identified DNA variations that are progressively enriched in frequency in response to selection. Polymorphisms resulting from intragenic variations within a dehydrin gene were identified and could potentially lead to the development of robust selection tools. Our results illustrate the benefits of feedback interactions between germplasm development programs and molecular physiology for a deeper understanding of the molecular and genetic bases of cold hardiness.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".