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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".