COMPREHENSIVE ASSESSMENT OF WINTER WHEAT-WHEATGRASS HYBRIDS BY CROP STRUCTURE ELEMENTS AND BREEDING INDICES
Bibliographic record
Abstract
In order to select the best forms in breeding practice, the analysis of lines according to breeding indices is widespread, which makes it possible to identify samples according to the complex of studied characteristics against the background of certain soil and climatic conditions. The article presents data on the assessment of winter wheat-wheatgrass hybrids from the collection of the Distant hybridization department of the MBG RAS by crop structure elements and breeding indices for 2022–2024. On average, over the years, the height of plants ranged from 75.1 to 102.5 cm, the length of the ear – from 8.3 to 10.8 cm, the number of grains per ear – from 26.7 to 48.5 pcs, their weight – from 1.47 to 3.24 g per ear, the weight of 1000 grains – from 44.1 to 58.6 g, biological yield – from 513.5 to 1232.5 g/m2. Nine samples were identified according to the Finnish-Scandinavian index, three according to the Mexican index, and ten according to the Canadian index. The value of the linear density index of the ear above 4 pcs/cm had twenty-one lines, four lines were distinguished in relation to the size to the number of grains in the ear. Three lines had the highest productivity index. According to the totality of all the signs, the best lines were identified: WWH 43, WWH 49, WWH 50, WWH 51, WWH 57 and WWH 90. These samples are recommended for further study and management of the breeding process.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".