APPLICATION OF CLUSTER ANALYSIS TO DETERMINE THE BREEDING VALUE OF LENTILS (Lens culinaris Medik)
Why this work is in the frame
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Bibliographic record
Abstract
The article presents the results of a study of agronomic characters of lentil, carried out in the fields of the A.I. Barayev Scientific and production center for grain farming JSC in 2021-2023. The objects of the study were 100 varieties from the genetic collections of lentil from the Institute of Plant Industry, ICARDA and foreign varieties (Turkey, Canada, Bulgaria, Moldova, Ukraine, Belarus). The Shyraily variety was adopted as a standard for large-seeded lentils, and the Krapinka variety – for small-seeded lentils. As a result of the research, sources of certain agronomic characters of lentils in the conditions of the Northern Kazakhstan were identified. Hierarchical clustering of the main components based on important agronomic characters revealed the presence of five groups with different breeding values. The most promising in practical and breeding terms are the samples belonging to the first cluster, which exhibit the highest expression of such quantitative characters as optimal yield and seed weight per plant. The second cluster includes productive and earlymaturing samples, while the samples in the third cluster can be used as sources of high protein content. Lentil samples from the fourth and fifth clusters may serve as promising parent material for the development of new lentil varieties.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it