Screening of breeding material of naked barley for breeding-valuable traits in the conditions of the Rostov region
Bibliographic record
Abstract
Abstract The Southern Federal District is one of the main grain-producing regions of the Russian Federation. The share of the Southern Federal District, in which the Rostov Region is located, accounts for 12-15% of the total Russian barley production. In solving the problem of a further increase in yield and an increase in the quality characteristics of grain, the priority belongs to the variety. As a result of a long-term study of the collection of naked barley, sources of valuable traits and properties were identified: high grain content of an ear: K-9010 (Turkey), Akka (Israel), Nuda Bianco (Italy), 1057-1923 (Czech Republic), Buck CDC (Canada), K-266 (Pakistan); the number of productive stems per unit area: Kitaki-nadaka (Japan), K-11182 (Japan), K-3772 (Dagestan), Golozerny (RF); early maturity: Omsk golozerny 1 (RF), Brunee (Ethiopia), NB-owa (Nepal), K-11182 (Japan), Golozerny (RF), K-3038 (Turkmenistan), K-3426 (Japan), K-19103 (India), K-26598 (Ethiopia), K-266 (Pakistan), Korona Laschego (Poland). Over the years of research on a complex of traits, a number of samples have been identified that combine a high potential of grain productivity with resistance to lodging, different duration of the growing season and plant height: K-26598 (Ethiopia), 84469/70 (Czech Republic), CDC Dawn (Canada), Holozerny (RF), 1057-1923 (Czech Republic), Omsk Holozerny 1 (RF), K-6099 (Afghanistan), Akka (Israel), Kitaki-hadaka (Japan).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".