An Indoor Screening Method for Improvement of Freezing Tolerance in Alfalfa
Bibliographic record
Abstract
Freezing tolerance is a determinant factor of persistence of alfalfa ( Medicago sativa L.) grown in northern climates. Selection for winter hardiness in field nurseries is difficult because of the unpredictability of the occurrence of test winters allowing the identification of hardy genotypes. A method of selection entirely performed indoor in growth chambers and walk‐in freezers has been applied for the identification of genotypes with superior freezing tolerance. Using that approach, cultivars recommended for growth in eastern Canada have been submitted to cycles of recurrent selection to generate populations potentially more tolerant to freezing (TF). Subsequent determination of the freezing tolerance of populations recurrently selected using plants acclimated to natural hardening conditions in an unheated greenhouse revealed a progressive increase in response to this selection approach. Field assessment of TF populations also showed better survival and forage yield than original cultivars at sites that experienced severe winter conditions. At stressed sites, a significant proportion of the variance in the yields of the populations was explained by freezing tolerance potential. Our results show that major increases in freezing tolerance (between 3 and 5°C) of alfalfa and better survival to severe winter conditions in the field can be achieved by screening for freezing tolerance under indoor growing conditions and intercrossing the selected plants.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".