Оценка параметров экологической адаптивности образцов люцерны по признакам «урожайность зеленой массы» и «содержание сырого протеина»
Bibliographic record
Abstract
The current paper has presented the estimation results of ecological adaptability of the alfalfa samples. The purpose of the work was to assess the productivity and quality of green mass of the alfalfa samples from the IPI plant genetic resources gene bank and to identify the most adaptive ones according to the trait ‘green mass productivity’ and ‘raw protein percentage’. The study of the collection alfalfa samples was carried out in the southern part of the Rostov region on the plots of the “ARC “Donskoy” in the breeding crop rotation of perennial grasses in 2016–2018. The objects of study were 30 alfalfa samples from the collection of N.I. Vavilov IPI from different countries (Canada, the USA, Peru, France). The variety ‘Rostovskaya 90’ was used as a standard one. The estimation of alfalfa samples on the presence of adaptive properties in them according to the trait ‘green mass productivity’ showed that the most valuable samples in present practical breeding work are the samples ‘K-32873’, ‘K-33299’, ‘K-42684’, ‘K-42249’, ‘K-78803’ with weak responsiveness to changes in environmental conditions; the samples ‘K-36104’, ‘K-48778’, ‘K-42694’, ‘K-45715’, ‘K-47800’, ‘K-47802’, ‘K-43260’ with high resistance to stress; the samples ‘K-43272’, ‘K-50545’, ‘K-47806’, ‘K-47807’ with genetically flexible genotypes. When breeding according to the trait ‘raw protein percentage’, the samples ‘K-47807’, ‘K-47804’, ‘K-42712’ possessing a high raw protein percentage and resistance to changes in this trait are important for further work.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
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".