Using breeding indices for evaluating collection samples of spring barley Hordeum vulgare L.
Bibliographic record
Abstract
The article presents experimental data on determination of adaptive properties and productivity of Hordeum vulgare L. ssp. distichon (L.) Körn. and H. vulgare L. ssp. vulgare samples of different ecological and geographical origin on the basis of breeding indices. Under contrasting conditions of the growing seasons, 2022 and 2023, 35 samples were studied for elements of grain productivity (ear length, number of grains in the ear and their weight, plant height). The field study was carried out at the experimental site of the biostation University of Tyumen “Lake Kuchak” (Nizhnetavdinsky District, Tyumen Province). The evaluation of productivity in the relationship “genotype — environment” showed that the following breeding indices are the most informative: Canadian, Mexican, linear ear density, plant productivity. On the basis of point ranking on the complex of indices, the best samples were: Zernogradsky 813, k-30453, Abalak, k-31201, Russia; Knezsa 65, k-22809, Hungary (var. erectum, nutans); Rokkaku-yabane, k-10986, Japan (var. brachyatherum). Higher yields were obtained under relatively favorable growing conditions in 2022, up to 439.8 g/m2 for double-row and up to 454.8 g/m2 for multi-row barley accessions; under stress conditions, up to 455.4 and 218.1 g/m2, respectively. Under stress conditions in 2023 compared to 2022, the multi-row barley samples showed an increase in the strength of correlation of yield with Canadian index (r = 0.73), plant productivity index (r = 0.66), linear ear density index (r = 0.58), Mexican index (r = 0.52). Two-row samples showed weaker correlation of yield with these indices. The exception was the Mexican index, characterized by a stable correlation coefficient over the years (r = 0.35).
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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 | 0.001 |
| 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) | 0.001 | 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".