Seed productivity and adaptability parameters of the alfalfa samples in the south of the Rostov region
Bibliographic record
Abstract
The general climate change in the south of Russia makes the development of new varieties of grain crops, including forage grasses, with high resistance and adaptability to climatic stress factors extremely necessary. The breeding process of forage plant adapted to changing environmental conditions is seen as an effective way to allow crop production to cope with unexpected possible climate changes with the least possible losses. The specific reaction of plants to environmental conditions is of particular interest when studying collection plant samples of different gen[1]otypes, as well as when choosing varieties for cultivation in specific conditions. Studying the stability and adaptability of collection samples of forage grasses can also make it possible to use them in different regions. The purpose of the current study was to estimate alfalfa samples by the parameters of ecological adaptability and stability according to the trait ‘seed productivity’. The objects of study were 30 samples of the VIR collection from the USA, Canada, France and Peru. There has been identified a large group of samples with bi < 1. These samples were important as genotypes with a weak responsiveness of seed productivity to worse conditions. They are to be used in hybrids to obtain an initial material that is adaptive according to the trait ‘seed productivity’ in stressful conditions. The smallest bi coefficient was found in the samples ‘K-42694’ (0.20), ‘K-32783’ (0.22) and ‘K-47804’ (0.29). The stability coefficient σd 2 , which reflects the correlation between the growing conditions and seed productivity of the samples through the years of study and was calculated on the basis of the theoretical productivity and the deviation of the theoretical value from the actual one, varied from 0.01 to 74.70. This range of variation indicates that the set of samples contains such samples whose stability of productivity is genetically determined and significantly exceeds the variability of the average productivity of the entire set. Estimation of differences according to stability of seed productivity, in comparison with the standard variety ‘Rostovskaya 90’, revealed a significant difference in this trait in the samples ‘K-43272’, ‘K-50545’, ‘K-50561’.
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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".