The importance of a comprehensive assessment of breeding indices and stress resistance parameters of winter rye varieties
Bibliographic record
Abstract
Abstract. Among grain crops, winter rye is one of the most popular crops, well adapted to the conditions of the North-West. Currently, most breeders use various selection indices in conjunction with the definition of stress resistance. Purpose. The aim is to study the collection samples of winter rye by yield and elements of the productivity structure and to identify the most adaptive for breeding in the conditions of the North-Western region. Methods. The experimental part was carried out at the VIR pilot site during 2018-2021 located in the North-Western region. The following were determined: Mexican index (Mx), Canadian index (Ki), linear ear density index (LPC), plant productivity index (IPR), Finnish-Scandinavian index (FSj), prospects index (J.P), index of the ratio of the mass of 1000 grains to the number of grains in the ear (j). The calculation of stress resistance parameters was carried out using the following indicators: stress resistance (Ymin- Y max), genetic flexibility (Ymin+Ymax)/2, stability of the variety (Ymin/Ymax), stress resistance coefficient (Kst.) was determined by A.A. Bykov. Results. The best conditions for the formation of yields were in 2020 and 2021 (Ij = +2.03; +1.8). In 2018 and 2019 – unfavorable (Ij = -1.3; -2.5). The maximum yield was obtained in 2020 in diploid rye varieties: Ophelia (10.3 t/ha) and Yaselda (9.0 t/ha), in tetraploid varieties Siberia 4. The results of these studies showed that the greatest advantage have variety: Siberia (Ʃ rank= 41), Eureka (Ʃ rank=55), ilmenite (Ʃ rank= 55), Ophelia (Ʃ rank=56), Siberia 4 (Ʃ rank= 58), Verisim (Ʃ rank= 59). Scientific novelty lies in the study of 12 varieties – seven diploids and five tetraploid winter rye. Thanks to the evaluation of varieties, they are divided according to the reaction of varieties to the environmental condition, stress-resistant and non-resistant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".