Agronomical evaluation of low dormancy alfalfa populations selected by an indoor screening method
Bibliographic record
Abstract
Abstract Fall dormancy is a vital component of alfalfa (Medicago sativa L.) yield in northern climates, but selection for the trait is often done at the expense of winter survival. We performed one cycle of selection to reduce fall dormancy in two winter hardy cultivars (Yellowhead and Peace) using a new indoor screening method. We compared the reduced dormancy populations with their respective initial cultivars for fall dormancy, yield, and winter survival at four sites across Canada. During the establishment and the first production years, plants of the reduced dormancy populations were generally taller in the fall than their respective cultivar, which resulted in a one unit increase of their fall dormancy class. Under field conditions, plants of the reduced dormancy populations had a similar winter survival than those of the initial cultivars. Under simulated winter conditions, freezing tolerance was not affected by selection for reduced dormancy in Peace, whereas a decrease from −24.0 to −21.5 °C was observed in Yellowhead. However, in this cultivar, we noted a 37% yield increase under field conditions and a 40% more vigorous regrowth under simulated winter conditions in the reduced dormancy population. These results showed that the indoor selection method effectively reduced fall dormancy and that indirect responses for yield and winter survival were dependent on the genetic background used as selection material. This selection method could therefore be promising to develop alfalfa cultivars adapted to northern latitudes with high winter hardiness and improved late season yield.
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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 | 0.000 |
| 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 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".