Grain quality of samples of the spring triticale collection (× <i>Triticosecale</i> Wittmack)
Bibliographic record
Abstract
The results of testing (2018–2019) varieties and lines of the spring triticale collection in the south-east of the Republic of Kazakhstan are presented. The study was conducted on 70 samples of domestic and foreign breeding from around the world: Russia, Ukraine, Belarus, Poland, Moldova, Austria, Canada, Argentina, Mexico. The experiment was carried out according to the methodology of the state variety testing of agricultural crops. To identify sources of economically valuable traits of grain, the following indicators were studied: test weight, flour sedimentation, protein and starch content, and falling number. The most valuable samples were identified as the starting material for creating varieties for animal feed and baking. Sources of high test weight (13 samples), sedimentation (five samples), protein content (six samples), starch (eight samples) were selected as the starting material for breeding on grain quality. Based on the assessment of spring triticale collection samples for protein content, flour sedimentation and falling number, the following varieties were selected for baking: Ukro, Korovai Kharikvsky, Addax, No. 7 (Rovnya x Lotos), MX 107. Due to high starch content (above 60%) the following varieties were selected for animal feed: WANAD, Pollmer 2,1,1, Fahad 8-2*2//PTP, Rubik, L 5635, Mieszko, L-105/08, Siskiyou. A positive relationship between starch content and test weight, and a positive correlation of starch content and falling number was revealed. All samples of varieties of spring triticale in the south-east of Kazakhstan formed grain with high falling number in the range of 192–336 s and were rated as first-class grain.
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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) | 0.002 | 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".