Influence of Seed Coat Color Genes on Milling Qualities of Red Lentil (Lens culinaris Medik.)
Bibliographic record
Abstract
Efficient milling is the key economic trait for the red lentil industry. Various seed characteristics including seed coat color can influence milling characteristics. Four basic seed coat ground colors (green, gray, tan, and brown) of 16 red lentil genotypes from a common genetic background were compared to determine the effect of seed coat color genes on three key milling quality traits: dehulling efficiency (DE), milling recovery (MR), and football recovery (FR). These genotypes were grown at two locations in Saskatchewan, Canada for two years. DE, MR, and FR results varied depending on the seed coat color conferred by specific genotypes. Green and gray seed coat color (homozygous recessive tgc allele) genotypes had significantly higher DE and MR percentages compared to brown or tan seed coat types (homozygous dominant Tgc allele) depending on genotype interaction with site-year. Seeds with brown or tan seed coats had significantly higher FR percentages in two site-years. Red cotyledon lentils with uniform shape and green or gray seed coat color might be more profitable for millers who wish to maximize DE and MR of red lentil, but brown seed coat color might be preferable in terms of FR.
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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 | 0.000 | 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) | 0.001 | 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".