Evaluation of technological indicators of collection varieties of oats in the Tyumen region
Bibliographic record
Abstract
Abstract. This article presents the results of a long-term study of collectible varieties of oats in the Tyumen region. The purpose of the study is to study the collection of spring oats, which forms high technological indicators, to increase grain production and to improve its quality. Materials and methods of research. The experiment was conducted in 2019–2021 in the conditions of the Tyumen Region at the experimental field of the Northern Trans-Urals Research Institute (Russia), using generally accepted methods of analysis and standard techniques. 167 varieties of oats of various ecological and geographical origins were evaluated according to the main technological indicators (grain type, weight of 1000 seeds and filminess), the Otrada variety was used as a standard. Results. A positive relationship of grain yield with the mass of 1000 seeds was established for all years of study (r = 0.21…0.45), a negative relationship was observed for all years with the film content of grain (r = –0.21; –0.31; –0.36). Regression analysis determined a significant positive effect on increasing the yield of 1000 seeds, negative – film content. Varieties of oats with a consistently high natural grain weight were identified: k-15272 (USA), 15254 (USA), k-15234 (Lithuania), k-15340 (Omsk region), weight of 1000 seeds: k-15278, (Moscow region), 15013 (Altai Krai), k-15330 (Ulyanovsk region), k-14402 (USA). As well as a low percentage of films: k-15301 (Canada), k-15272 (USA), k-15280 (Moscow region), k-15048 (Finland). The distinguished varieties of oats are particularly valuable for a number of reasons, they can be used in breeding work as sources, and they have high potential and product quality: k-15013 (Altai Krai), k-13911 (Leningrad region), k-15330 (Ulyanovsk region), k-15425 (Germany), k-15272 (USA), k-15301 (Canada). Scientific novelty. A comprehensive assessment of 167 varieties of spring oats was carried out and the most promising ones were identified, differing in the best technological quality indicators, which can be recommended in breeding work in order to improve the quality of grain, not inferior in yield.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".