Sources of economically valuable traits for covered oat breeding
Bibliographic record
Abstract
Using of genetic diversity is one of the factors of successful oat breeding. Screening of sources with high yield, resistance to abiotic (high soil acidity and drougth) and biotic (pathogens) stressors, early maturity, high grain quality is actual for Volga-Vyatka region. Studies were conducted in contrast climatic conditions on favorable and natural acid (provocative) soil backgrounds in Kirov region. The most promising accessions are 15068 Konkur (Russia) and 14983 Hybrid (Mexico) having resistance to soil acidity and drought and high grain quality. Accessions 15069 Rysak (Russia), 14967 Florida 657 (USA), and 15082 Ivory (Germany) are recommended to include in crossing with adaptive varieties of local breeding. Sources are selected having high resistanse to soil acidity and high grain quality: 15065 Irtysh (Russia), Yakov (Russia), 15127 SW Betania (Sweden), 15052 Rigja (Norway), 14966 Winter Dum (South Africa) etc. For breeding to early maturity accessions 22h10 Eaton (Russia), 14964 Zwarte president (Netherlands), 15257 RA 7836-416, 15258 RA 7836-2701, 15264 RA 7967-11690, 14620 Newman (USA), 15111 L-15 (Colombia), 15027 C.I. 9101 (Turkey), 14991 ОА 309 (Canada) etc are recommended as paternal forms. Sources of high grain quality and resistance to pathogens are 15184 AC-7 (Russia), 14970 Illinois 62-1532 (USA), 14535 Urano Inia (Chile) etc.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".