Ecological adaptability parameters of the alfalfa varietal samples according to the trait “seed productivity” in the Rostov region
Bibliographic record
Abstract
Relevance. Adaptive plant breeding is considered an effective and affordable tool that allows to develop varieties that are weakly responsive to the emerging unfavorable conditions. Methodology. The current study of the alfalfa samples was carried out on the experimental plots of the ARC “Donskoy”. The objects of research were the varieties of the ARIPGR (VIR) collection (11 samples from the USA, 16 samples from Canada, 2 samples from France, 1 sample from Peru). Results. Estimation of the alfalfa varietal samples according to some ecological adaptability parameters according to the trait “seed productivity” showed that the varietal samples from this collection respond differently to the environmental changes in their cultivation. The genotypes K-32783, K-42684, K-42685, K-48773, K-48774, K-48775, K-42694, K-45119, K-45715, K-47800, K-47801, K-47802, K-47803, K-47804, K-39978 and K-42712 had bi < 1 and hardly responded to the changes in growing conditions, and they had stable, although not very high, seed productivity. There have been identified the samples K-32783, K-42694, K-47801 and K-42712 that demonstrated their resistance to stress conditions. The samples K-50545 and K-50561 turned out to be genetically flexible, their “compensatory ability” better corresponded to the environmental factors. The genotypes K-43272, K-50545 and K-50561 were characterized with higher coefficient (σd 2) of the productivity stability in comparison with the standard variety.
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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 | 0.001 |
| 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) | 0.000 | 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".