Study of soft wheat varieties in the southern part of the Volga-Vyatka region
Bibliographic record
Abstract
The article provides the results of studying the varieties of spring soft wheat (Triticum aestivum L.) of various ecological and geographical groups in the conditions of the Chuvash Republic for 2016-2019. The objects of the study were presentedby 72 varieties and variety samples of spring soft wheat of Russian and foreign selection (Belarus, Canada, USA, Kazakhstan,Germany, Poland, Ukraine and Australia). The Simbircit variety zoned in the Volga-Vyatka region (Russia) was used asstandard. Low adaptability of most of the studied varieties to the soil and climate conditions of the region due to the strongvariability of yield was established. Two Russian varieties Arhat and Icarus with a relatively high coefficient of adaptability(0.71-0.72) exceeded the standard variety in yield by 0.68 and 0.67 t/ha or 18.2 and 18.0 %, respectively. By productive bushiness (53.8-61.5 % higher than the standard), Binni (Australia), Mercana and Omskaya 41 (Russia) were distinguished; byplant height (10.6-14.7 %) – Ekada 113, Mercana and Yulia (Russia); by ear length (13.4-22.0 %) – Mutant ostistyj (Belarus),Raduga and Mis (Russia); by the number of grains in the ear (25.3-34.3 %) – Icar, Ekaterina and Agata (Russia); by weightof grains in the ear (14.8 %) – Arhat and Ekada 113 (Russia); by weight of 1000 grains (4.5 % higher than the standard) –Margarita (Russia). Twelve varieties with strong relation between yield and elements of productivity (R ˃ 0.7) have been selected.
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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".