Application of molecular genetic methods in breeding of small-seeded lentils for suitability for mechanical harvesting
Bibliographic record
Abstract
The lack of local varieties, as well as low competitiveness and insufficient technological efficiency of lentil varieties of various geographical breeding, determine the necessity for faster improvement of the crop in terms of suitability for mechanized harvesting. The use of markers allows reducing significantly the time required for breeding varieties with the desired indicators. The current study was aimed at searching for KASP markers associated with technological traits in collection samples, as well as identifying effective SNP loci for use in marker-assisted breeding of lentilin Western Siberia. There has been found that aridity in 2023 was favorable for growth and development, since there has been established a more compact bush of the lentil plant due to a weak degree of branching (1–4 branches of the first and subsequent order), a foliage degree of less than 60 % and a mean daily growth of less than 0.70 cm per day and less cracking of beans (10.93 %). Genotyping has demonstrated a statistically significant effect of branching and foliage (LcRBContig00050 and LcRBContig00065) on increasing the lodging resistance of lentil agrophytocenosis, expressed in a vegetative mass decrease by 10–30 %. The favorable allele of the growth rate markers (LcRBContig00079 and LcRBContig00158) has statistically significantly increased the average daily plant growth by 0.35–0.91 cm at the initial stages of development. The KASP markers LcRBContig01123 and LcRBContig0534 have made a significant contribution to increasing the plant height by 2–8 cm and the height of the lower beans’ attachment by 1–4 cm. The SNP (LcRBContig00067) associated with the non-cracking of beans allows increasing the percentage of non-cracking lentil beans during maturation to 90 %. As a result, there have been selected the small-seeded lentil samples with a set of genes responsible for suitability for mechanized harvesting, reliably surpassing the standard in terms of technological effectiveness, such as ‘Orlovskaya Krasnozernaya’, ‘Severnaya’, ‘Rubinovaya’ (Russia), ‘Krapinka’ (Kazakhstan), ‘Pardina Linsen’ (Germany), ‘KDC Kermit’, ‘Redcap’ (Canada).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".