EVALUATION OF SOFT SPRING WHEAT LINES AND VARIETIES BY BREEDING INDICES
Bibliographic record
Abstract
The studies were conducted in 2019-2023 to evaluate soft spring wheat samples for the main economically important traits and to identify valuable forms for breeding. The material for the study was 26 varieties and lines of spring soft wheat bred by Far Eastern Research Institute of Agriculture, standard – Khabarovchanka. The soil of the site is meadow-brown podzolic-gley heavy loamy with pHsalt less than 4.5 units. In May-July of 2019, the excessive waterlogging was noted (HTC=2.6), in 2021-2023 – drought conditions (HTC=1.0…1.4), in 2020 – within the average long-term values (HTC=1.7). According to grain yield (35.0…38.2 c/ha), the following varieties and breeding lines were distinguished: 21/1-01, 65/2-11, Zaryanka, 26/2-14, 19/1-14, 14/2-00, 29/8-06, 60/2-09, 11/7-14, Dalira, 48/2-14, 43/3-14, Elizaveta, 22/2-16, 47/1-14, which reliably exceeded the standard by 3.9…6.9 c/ha. The greatest influence on the productivity was exerted by the factor “year” - 67.8%, the effect of the factor “variety” reached 7.7%, their interaction - 24.5%. Promising for breeding work are the lines 94/3-09, 29/8-06, 55/5-09, 48/2-14, 19/1-14, 65/2-11, 26/2-14, 118/1-99, 47/1-14, 11/7-14, 65/1-17, 43/3-14, selected as a result of cluster analysis (Ward’s method) of standardized values of a number of breeding indices: Mexican, which characterizes resistance to lodging (MI = 0.012 ... 0.016); Canadian, allowing to estimate the specific ear yield (KI = 0.153 ... 0.171); Finno-Scandinavian, determining the grain-forming ability (FSI = 0.321 ... 0.383); linear density of the spike, reflecting the genotype-environment relationship (LPC = 3.769 ... 4.292); spike potential, which characterizes potential productivity (IPC = 0.076 ... 0.092); the ratio of size to the number of grains in the spike, demonstrating competitiveness (ICC = 0.976 ... 1.204); prospects, indicating breeding value (IP = 0.326 ... 0.390); plant productivity, which indicates resistance to unfavorable environmental factors (IPR = 7.757 ... 9.713). High-yielding lines with high selection value include 29/8-06, 55/5-09, 48/2-14, 19/1-14, 65/2-11, 26/2-14, 118/1-99, 47/1-14, 11/7-14, 65/1-17, 43/3-14.
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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".