Evaluation of oat varieties adaptive properties by productivity in the Priobskaya forest-steppe zone
Bibliographic record
Abstract
The results of evaluation of adaptive properties of released oat varieties of Siberian breeding by yield in the Priobskaya forest-steppe of Novosibirsk region are presented. The experiment was conducted on the experimental field of the Siberian Research Institute of Plant Production and Breeding in 2012-2021. The object of the study were 37 oat varieties included in the State Register of the Russian Federation and released in the West Siberian (№ 10) and East Siberian (№ 11) regions. Yield estimation and phenological observations were carried out according to the methodology of N.I. Vavilov All-Russian Institute of Plant Genetic Resources. For statistical data processing the method of B.A. Dospekhov was used. Potential productivity and adaptability of the varieties were determined by L.A. Zhivotkov et al. method, resistance of the varieties to stress conditions - by A.A. Goncharenko, the yield spread - by V.A. Zykin. Analysis of yields by groups of ripeness showed a natural tendency: as the growing season lengthens by groups of ripeness, the productivity potential also increases. The following varieties had high indicators of productivity potential in favorable years and adaptability to adverse environmental factors: Krasnoobsky, Mustang, Metis, Oven, Otrada, Talisman, Irtysh 21, SIR 4, Orion. The varieties Oven, Novosibirsk 5, CIR 4 showed high adaptability and stability. Four varieties were characterized by the ability to give not the maximum, but high stable yield under any conditions: Krupnozerny, Novosibirsk 88, Belozerny, and Korifey.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".