ИСХОДНЫЙ МАТЕРИАЛ ДЛЯ СЕЛЕКЦИИ СОРТОВ ЯРОВОЙ МЯГКОЙ ПШЕНИЦЫ В УСЛОВИЯХ КИРОВСКОЙ ОБЛАСТИ
Bibliographic record
Abstract
The paper demonstrates the research results on 237 varieties of soft spring wheat in Kirov region compared with highly-productive mid-ripening Simbircite. The author observed variety variation on vegetation period within 75-87 days, crop yield - 10.5 - 53.8 c/ha, plant height - 58-119 sm, productive tilling capacity - 1.0 - 2.7 stalk, mass of 1000 grains - 25.9 - 52.2 g, protein concentration - 7.6 - 18.3 % and fibrin concentration - 13.9 - 49.1 %. The article reveals varieties’ genotypic differentiation in dependence on their ecological and geographical origin. The varieties of the North-Western region differed in the length and grain content, the varieties of the Central region were characterized by high stalks, low bushiness, big head, high protein and fibrin concentration. The varieties of Volga selection can be applied as sources of grain quality, drought resistance and head productivity. The varieties of Western-Siberian region are highly productive, resistant to stress and adaptive; they form sufficient biomass due to their high bushiness and plants’ height. The varieties of Eastern-Siberian region are considered to be significant for investigation due to their being the sources of high crop yield and ripening. The researcher has explored 103 foreign varieties and has highlighted 56 valuable varieties. The varieties from Ukraine can serve as the sources of ripening and grain quality; the varieties from Kazakhstan show drought resistance, high protein concentration and productivity; Germany- high grains; Canada - high protein concentration and the fibrin of good quality. Varieties from China, Syria, Algeria, Tunisia, Mexico and India are low adaptive to the conditions of the Volga-Vyatka region, but their grain is of high quality and can be recommended to be used in the reciprocal cross and saturate crossing.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".