Interrelationship between yield of oat varieties and key traits in the Priobskaya forest-steppe zone
Bibliographic record
Abstract
This study presents the research results on the interrelationship between biological oat yield and its components in the Priobskaya Forest-Steppe zone of the Novosibirsk region. The experiments were conducted at the Siberian Research Institute of Plant Cultivation and Breeding - a Federal Research Center Institute of Cytology and Genetics SB RAS branch from 2012 to 2021. The material for the study consisted of 37 oat varieties included in the State Register of Breeding Achievements. It is approved for use in the West Siberian and East Siberian regions of the Russian Federation. The study aims to determine the priority elements of oat yield structure by ripening groups and varieties in contrasting meteorological conditions. Of the 37 types, three are early, 14 are mid-early, 19 are mid-season, and one is mid-late. All years of the study are divided into two categories based on the average yield over ten years, with favourable and unfavourable conditions for yield formation. It has been established that the density of productive stems remaining at harvest is the main element in oat yield formation for mid-early and mid-season varieties in the Priobskaya Forest-Steppe zone under suitable conditions. Under unfavourable conditions, the productivity of the ear is crucial. The study demonstrates the correlation between structural elements and the biological yield of varieties in contrasting conditions. The influence of stem density on yield in unfavourable conditions was observed in Tubinskiy, Phobos, Tayezhnik, Uran, Anchar, Mustang, and Barguzin. In contrast, it was evident in favourable conditions in varieties like Anchar, Otrada, Korifey, Togurchanin, Krupnozerny, and Kemerovskiy 90. A strong correlation between increased grain weight from the ear and higher yields was found in unfavourable conditions for varieties like Tarskiy 2, Tubinskiy, Otrada, Talisman, Orion, Phobos, Barguzin, Irtish 22, Uran, Pamyati Bogachkova, Altayskiy Krupnozerny, Narymskiy 943, Pegas, and in favourable conditions for varieties like Rovyesnik, Belozerny, Anchar. In both sets of states, a strong correlation was observed in types like Tayezhnik, Novosibirskiy 88, Tulunskiy 19, Irtish 21, Kemerovskiy 90, Korifey, Togurchanin, Sig, Tayezhnik, Krasnoobskiy, Sibiryak, Baykal, Novosibirskiy 5, Mustang, Irtish 13, Togurchanin, Krupnozerny, Sig. The impact of 1000-grain weight on yield was noted in unfavorable conditions for varieties like Krasnoobskiy, Sibiryak, Baykal, Anchar, Oven, Tubinskiy, Sig, Orion, Korifey, Uran, Novosibirskiy 88, Mustang, Togurchanin, Otrada, Altayskiy Krupnozerny, Irtish 21, Egorich, Narymskiy 943, and in favorable conditions for varieties like Novosibirskiy 88, Creol, Irtish 21, Belozerny, Metis, Irtish 13.
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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 | 0.000 | 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) | 0.001 | 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".