Adaptability of accessions of filmy oats in the collection nursery
Bibliographic record
Abstract
It is urgent to study the ecological and adaptive ability of oat samples in specific growing conditions to obtain high yields and resistance to biotic and abiotic stresses. In 2020-2022 in the conditions of Kirov region 47 samples of oats (standard - variety Krechet) were studied. Unfavorable conditions for the formation of yield were in 2021 (Ij = -1.2), relatively favorable – in 2020 (Ij = +0.4) and 2022 (Ij = +0.8). Yield of samples varied among the years, with the coefficient of variation varying from 2.9 % (к-14221 Рс 60, Canada) to 75.4 % (к-15536 UFRGS-11, Brazil). Twenty-four accessions, including к-15547, к-14649 (Russia), к-15464 (Kazakhstan), к-15533, к-15545, к-1554 (Brazil), к-15583, к-15585 (Sweden), etc., were responsive to improvement of growing conditions (bi>1). The amplitude of productivity changes is characterized by the stability index (Si 2 ). Stability of yield of samples к-15530 UFRGS-2, к-15541 UFRGS-17 (Brazil), к-13658 Рс 35, к-14221 Рс 60 (Canada) varied within 0.04...0.79. Samples from Canada (к-14221, к-13670), Brazil (к-15530, к-15541) and Sweden (к-15586) were the most stress tolerant. Genetic flexibility shows the reaction of plants to growing conditions, the maximum values of the trait (433 ...527 g/m2 ) were observed in samples к-15542 UFRGS-18 (Brazil), к-14648 Argamak (Russia), к-14397 Рс 67 (Canada), к-15583 Mutant 230, к-15585 Mutant 261 (Sweden). According to the results of the tests, 11 samples were selected for obtaining source material with the required parameters. The samples к-15541 UFRGS-17, к-15542 UFRGS-18, к-13662 Pc 45, к-14668 Pc 54, к-13187 Pc-56, к-14431 Pc 59, к-13658 Pc 35, к-14221 Pc 60, к-14396 Pc 64, к-14397 Pc 67 are recommended for inclusion in breeding programs. These samples are characterized by adaptability to varying growing conditions according to the "yield" trait.
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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 | 0.001 |
| 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 | 0.000 |
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".