The use of breeding indices when estimating winter durum wheat productivity
Bibliographic record
Abstract
Special attention is currently being paid to the role of genetic and physiological systems that contribute to improving productivity and can be estimated in the form of indices. The current study was carried out in the southern part of the Rostov region at the experimental plots of the FSBSI «ARC “Donskoy”» in 2021–2023 aimed at studying the breeding indices of promising winter durum wheat varieties and lines and their effect on productivity. There have been considered such breeding indices as Mexican, Canadian, ear potential, plant productivity and prospects. Considering the Mexican index, which reflects the capabilities of the mechanical tissues of straw, there have been identified 8 samples Lakomka, Khrizolit, Pridonie, 536/19, 971/19, 1147/19, 1174/19, 1037/17 with high values (0.020–0.023 g/cm) in comparison with the standard variety Kristella. The Canadian index ranged from 4.93 pcs./cm for the standard variety to 7.13 pcs./cm for the variety Khrizolit. According to ear potential index, the varieties and lines varied from 0.082 cm/cm for the standard variety Kristella to 0.097 cm/cm for the line 536/19. The index of prospects shows the ability of straw to transport plastic substances into grain. According to this indicator, the studied samples ranged from 42.7% (the variety Grafit) to 55.4% (the line 536/19). According to the classification, the winter durum wheat varieties and lines varied from low (IPR < 7.0; grain weight per ear was up to 1.5 g) to high productivity (IPR > 11.0; grain weight per ear was more than 2.0 g). In these studies, there was a significant correlation between yield and the ear potential index (r = 0.57 ± 0.22).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".