YIELD AND FEATURES DETERMINING PRODUCT QUALITY IN SAMPLES OF ALFALFA COLLECTION
Bibliographic record
Abstract
The research was carried out in order to identify collection samples of alfalfa (M. sativa) and variable (M. sativa x M. varia), promising for breeding in the south-east of Kazakhstan. Sowing was carried out in the spring of 2019, by a coverless method, calculations for 2019-2021. The material for the study was 134 varieties of various ecological and geographical origin, the standard is the local variety Semirechenskaya local. As a result of the study of genotypes, samples of alfalfa (M. sativa) showed good bushiness: (k-14) from the USA, (k-5677) Italy, (k-315) France, (k-5677) Italy, (k-5677) Italy, k-267) Uzbekistan, more than the standard for three years on average by 11-12 pieces. Whereas in alfalfa the following samples distinguished themselves: (k-39932) from Canada, (k-26713) Ukraine, (k-47492) Kazakhstan, (k-23206) Ukraine (k-34627) Kazakhstan, these samples exceeded the standard by an average of 3 – 5 pieces. In terms of foliage in alfalfa, the highest indicators were in samples: (k-45479) from Russia and (k-5677) Italy, as well as in alfalfa variables (k-31885) Russia, (k-33299) Canada, (k-39932) Canada, (k-61324) Kazakhstan. In both species of alfalfa, leafiness varied within 51.0 – 52.3%, the excess over the standard was 23.2-36.5%.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".