Productivity and parameters of ecological adaptability of alfalfa samples under the conditions of the South of Russia
Bibliographic record
Abstract
Introduction . The initial material is the basis of the current breeding work with all agricultural crops, including alfalfa. The purpose of the conducted work is to estimate the productivity of alfalfa samples in the collection nursery, depending on the growing conditions and the identification of the most adapted samples according to the trait “green mass productivity”. Materials. The objects of the current study were 30 alfalfa samples (16 samples from Canada; 11 samples from the USA; 1 sample from Peru; 2 samples from France) from the collection ARIGCR named after N.I. Vavilov. Results. The estimation of alfalfa samples for the presence of adaptive properties based on the trait ‘green mass productivity’ showed that: — the genotypes К-27116, К-43269, К-43272, К-48771, К-48775, К-48776, К-50545, К-50561, К-45119 are more responsive to changes in environmental conditions; the genotypes К-32873, К-33299, К-42684, К-42249, К-47803 are characterized with a slight b i < 1 response to changes in environmental conditions; the genotypes К-36104, К-48778, К-42694, К-45715, К-47800, К-47801, К-47802, К-43260 are characterized with high stability to stresses; the genotypes К-43272, К-50545, К-47806, К-47807 are characterized with genetic adaptability; the genotypes К-36104, К-48778, К-48715, К-47800, К-43260 are characterized with more stability of response to changes in environmental conditions; the genotypes К-36104, К-48778, К-45715, К-47800, К-47801, К-47802, К-39978, К-43260 are characterized with great homeostasis (ecological adaptability).
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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 | 0.000 | 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".