Создание новых сортов ярового ячменя для использования на кормовые цели
Bibliographic record
Abstract
The aim of the study was to create barley breed-ing material for feeding purposes. The work was performed under conditions of the Krasnoyarsk for-est zone. The best varieties were tested on the ex-perimental fields of Krasnoyarsk region and Tyu-men region. The Acha variety was the standard of corn yield, Sobolyok was for the green mass yield. The initial stock in the hybridization breeding was presented by varieties Krasnoyarsky 80, Kedr, So-bolyok selected by Krasnoyarsk research institute of Agriculture and multiserial sources of resistance against loose smut, they were Conquest, Bonanza, Paragon, Heartland №1 (Canada), Woodvale, Luther (USA). New varieties were crossed with each other and with new, more productive varieties of barley. The strict selection for productivity, resis-tance against loose smut and the green mass yield was conducted during the breeding work. The va-riety Krasnoyarsky 91 stood out under conditions of Krasnoyarsk region. It is a pallidum variety. This is a middle-late variety with the vegetation period of 75–85 days. During 2012–2015 average productivi-ty of grade was 30.4 centner/hectare from 25.4 to 40.5 centner/hectare), it was 4.4 centner per hec-tare higher than the standard Acha and 5.6 centner per hectare than variety Sobolyok. Krasnoyarsky 91 exceeded Sobolyok by green mass yield by 54 centner/ hectare. The variety Uvatsky was the best in corn yield under conditions of the Tyumen region. It was a pallidum variety. This is a middle-late variety. During 2013–2015 average productivity of grade was 69.1 centner/ hectare (from 59.4 to 83.7 centner/hectare) it was 14.4 centner per hec-tare higher than the standard Acha . In a green mass yield Uvatsky exceeded Sobolyok and had one slightly less than Krasnoyarsky 91. The superiority of multi-row varieties by a green mass yield was re-vealed. New varieties of barley exceeded in grain productivity the variety of grain direction Acha and in the green mass yield they exceeded Sobolyok.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.015 | 0.006 |
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".