Продуктивность и адаптивность сортов овса в условиях Приобской лесостепи
Bibliographic record
Abstract
Results are given from studies, conducted in 2010-2014, on 118 spring oat accessions in the nurseries of the second, third and forth years of study. Resulting from the evaluation of the material for adaptation power and ecological adaptability under conditions of the forest-steppe areas near the Ob, there were identified the genotypes with high ratios of their yields to the average yield among varieties according to maturity groups. Of medium-early genotypes, the following varieties were distinguished by high adaptation to unfavorable environmental factors: Atego (Czech Republic), Tigrovy (Khabarovsk Territory), Dagny (Sweden), Local Romanian K-14951, Ivory (Germany), Novosibirskiy 5 (Novosibirsk Region), Togurchanin (Tomsk Region). They were characterized by high ratios of their yields to the varietal average. Of mid-ripening accessions, the following varieties were distinguished by higher yields compared to the group average during the years of study: AC Morgan (Canada), Gunter (Kirov Region), Konkur (Ulyanovsk Region), and R8 N/9 3037-3072 (Krasnoyarsk Territory). Among naked oats, Vyatsky Golozerny (Kirov Region), MF 9424-62 (USA), Aldan (Kemerovo Region), Krepysh (Belarus) were distinguished. The standard varieties had the minimum range of variation in contrasting years. As to productivity stability, there were distinguished Togurchanin (Tomsk Region), Atego (Czech Republic), and Tigrovy (Khabarovsk Territory) in the group of medium-early varieties, and Konkur (Ulyanovsk Region) among mid-ripening varieties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".