RELATION BETWEEN THE CROP YIELD AND PRODUCTIVITY ELEMENTS OF LENTIL
Bibliographic record
Abstract
Lentils (Lens culinaris Medic.) belongs to the group of valuable high-protein food crops. A significant role in increasing its productivity is given to breeding. Among the methods of statistical data processing, the analysis of correlation interconnection between characteristics has become widespread in selection. Therefore, the aim of the research was to determine the correlation between the main economically valuable traits of lentil collection samples in the conditions of the Omsk region. Research project was carried out in 2016–2018 on the sidelines of the training and experimental farm of the Omsk State Agrarian University, located in the Southern forest-steppe of Western Siberia. The object of the study was the collection samples of lentils of different ecological and geographical origin (Russia, Germany, Turkey, Canada, Bulgaria, Moldova, Ukraine, Belarus, Kazakhstan). The standard was the Aida variety. Over the years of research, according to the results of the correlation analysis, a stable interconnection between the yield and the number of beans (r = 0.80 ± 0.04) and the mass of seeds (r = 0.80 ± 0.04) per plant was fixed. An average positive interconnection between yield was found during the periods from germination to flowering (r = 0.60 ± 0.09) and from flowering to ripening (r = 0.60 ± 0.09), the number of seeds in a bean (r = 0.60 ± 0.09), weighing 1000 seeds (r = 0.50 ± 0.09), the distance from the tip of the lower bean to the soil (r = 0.40 ± 0.08) and the height of the plant (r = 0.40 ± 0 , 08). A weak positive dependence of the yield was fixed on the length of the bean (r = 0.30 ± 0.07) and the height of attachment of the lower bean (r = 0.30 ± 0.07). The analysis showed the degree of influence of various elements of productivity on the formation of productivity, which affords more targeted selection in the selection process.
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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".