Promising triticale breeding lines for the Far Eastern region
Bibliographic record
Abstract
The main problem in selection is to improve the efficiency of selection of the source material (base line), so the use of selection indexes allows you to optimize the comprehensive assessment of variety species main economic and biological characteristics. The following indices were used: Mexican, Canadian, Finno-Scandinavian, linear density of the ear, the ratio of seed size to the number of grains in ear, productivity and potential of plants. As a result of research, it was found that new breeding lines of triticale of spring forms in the climates of the Far Eastern Region (Khabarovsk Territory) realize their productivity potential significantly higher than the standard Ukro variety. In the course of the research we identified the most promising triticale breeding lines with high yield and optimal formation of quantitative characteristics and structural elements of productivity. The maximum crop yield (13.4 t/ha) in agricultural environment of the region was produced by the highly productive variety number 1548-19 (Ukro x Dalgau 1). The greatest realization of the potential crop yield in breeding species 1546-19 (Ukro x Lana) and 1548-19 (Ukro x Dalgau 1) was due to the productivity of the ear having large number and weight of grains in the ear (6.2 and 7.9 t / ha, respectively). High relationship between the index of linear ear density and crop yield of triticale genotypes (r=0.574) indicates a significant influence of ear productivity on the formation of this characteristic. Comprehensive assessment of selection indices showed a promising line of spring triticale - 1546-19 (Ukro x Lana), having high selection value and an optimal system of adaptive reactions to vegetation conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".