ASSESSMENT OF PLATE LENTIL SAMPLES BY ECONOMIC AND BIOLOGICAL INDICATORS
Bibliographic record
Abstract
The studies were conducted in 2021-2023 in Omsk region to evaluate samples of plate lentils of domestic and foreign selection and to identify sources of economically valuable indicators. The material for the study was 32 samples of large-seeded lentils, the standard being the mid-early Aida variety. The soil of the site is meadow-chernozem, medium-deep, low-humus, medium loamy with an environment close to neutral reaction (pH 6.5). In 2022 slightly arid climatic conditions were noted (HTC = 1.02), in 2021 and 2023 - very arid (HTC = 0.68 and HTC = 0.75, respectively). The preceding crop is spring wheat. Under conditions of significant influence (66.2…74.6 %) of weather conditions, the best samples in terms of a set of economic and biological characteristics were identified: k-2692 (Russia, Penza region), Rozovosemyannaya (Russia, Penza region), Sovereing (Canada), Shyraily (Kazakhstan), Anfiya (Russia, Penza region), Glamis (Canada), Penzenskaya 14 (Russia, Penza region), k-2980 (Russia, Leningrad region). The distinguished samples were distinguished by the shortest, in comparison with the standard, duration of the vegetation period by 5…9 days. They also produced, compared to the standard, a significantly higher (p≤0.05) number of beans per plant (by 9...16 pcs.), seed weight per plant (by 203.8...228.8 %), and 1000-seed weight (by 11.31…13.14 g). In addition, they demonstrated a significant superiority over the standard in protein content – 1.60...1.82 %, fat – 0.35...0.37 %, ash – 0.59...0.66 %, fiber – 0.25...0.3 %, and NEV – 0.89...0.98 %. The bean length was at the standard level (17 mm), the average seed diameter was 7 mm, the peel color was predominantly green, and the seeds cooked well (40…60 minutes). It is advisable to use these samples in the region as sources of valuable traits for breeding high-yielding varieties of lentils.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".