APPLICATION OF CLUSTER ANALYSIS TO DETERMINE THE BREEDING VALUE OF LENTILS (Lens culinaris Medik)
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".