Breeding estimation of initial alfalfa material according to green mass productivity and quality
Bibliographic record
Abstract
Breeding of perennial grasses is the foundation for developing a forage base to produce high-quality livestock products. The purpose of the current study was to estimate the variability of phenotypic traits of alfalfa collection populations, as well as to identify the most promising ones in terms of possession of important agronomic traits for developing varieties that meet modern requirements of agricultural production. There have been estimated economically valuable traits of 80 alfalfa populations of various ecological and geographical origins from the collection of the FSBSI “ARC “Donskoy” for the period 2019–2023. The variety ‘Rostovskaya 90’ was used as a standard. There was determined a biochemical analysis of the green mass of alfalfa collection samples, including content of protein, fat, ash, fiber, and NFE, and there was carried out a statistical analysis of the experimental data. As a result of the research, there were identified the samples ‘Smuglyanka’ (Ukraine) – 7.9 kg/m2 , ‘Rambler’ (Canada) – 7.7 kg/m2 and ‘Stavropolskaya 430’ (Russia) – 7.6 kg/m2 with large productivity of green mass. The samples with high indicators of green mass quality ‘Tibetskaya’ (Kazakhstan), ‘Sinegibridnaya 1316’ (Russia), ‘Stavropolskaya 430’ (Russia), ‘Rhizoma’ (Canada), ‘Rambler’ (Canada), ‘VNIIOZ-16’ (Russia), ‘Smuglyanka’ (Ukraine), ‘Karlygash’ (Kazakhstan), ‘Prowler’ (USA), ‘Sarga’ (Russia), ‘G-4’ (Russia), ‘Donskaya 5’ (Russia), ‘Sin 4’ (Russia), ‘Sin 5’ (Russia) and ‘Sin 6’ (Russia) have been recommended for breeding programs to develop alfalfa varieties with large productivity of green mass and nutritional properties of dry matter.
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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 | 0.001 |
| 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".