Estimation of economic and biological traits of the alfalfa initial material in the south of the Rostov region
Bibliographic record
Abstract
Alfalfa is a perennial legume that plays an important role in feed production. The high demand for alfalfa all over the world, including the Russian Federation, results in the necessity to develop new high-yielding varieties with high quality feed. The purpose of the current study was the morpho-biological analysis of 198 alfalfa collection nursery samples (sown in 2018) and the identification of sources of useful economic and biological traits in comparison with the standard variety ‘Rostovskaya 90’ (Russia). The study was conducted in 2019-2021. Based on the study results there have been identified the following sources of useful traits: Pickstar (Canada), Saranac A.R. (USA), G118/13 (Russia); according to plant height (105-107 cm); Caraveli (Peru), Saranac A.R (USA), Liska (Ukraine), Sarga, G 19/13, G 144/13, Selection 5, Sin 6, Sin 36/95 (Russia) according to foliage (over 50 %); Selection 79, Uralochka, G-3, G-5, Donskaya 5, G 97/13, G 8/13, G 73/13 (Russia); according to green mass productivity (4.83-5.79 kg/m2 ); Saga (Canada), Selection 6, Sin 1, d. 14813, G-2, Sin 36/95, Selection 33, Selection 34, d. 4576 (Russia) according to dry matter content (over 29 %); Sarga (Russia), Karlygash and Aliya (Kazakhstan) according to crude protein content (over 21 %). The identified samples will be used as parental forms in alfalfa breeding for feed productivity.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".