Genetic Mapping of Milling Quality Traits in Lentil ( <i>Lens culinaris</i> Medik.)
Bibliographic record
Abstract
Milling qualities are key traits for the red lentil ( Medik.) industry as price is largely determined by recovery yield. milling involves removal of the seed coat and splitting of the cotyledon to produce either splits or footballs (cotyledons still attached). The objectives of the study were to determine the heritability of the milling traits dehulling efficiency (DE), milling recovery (MR), and football recovery (FR) and to identify the genomic regions controlling them. We used a lentil recombinant inbred population from the cross 'CDC Robin' × '946a-46', which have contrasting seed characteristics. The mapping population consists of 127 F-derived lentil recombinant inbred lines that were phenotyped for milling quality parameters from four site-years in Saskatchewan, Canada. A total of 534 single nucleotide polymorphism markers, seven simple sequence repeat markers, and four morphological markers were used for quantitative trait locus (QTL) mapping. The broad-sense heritability was moderate for DE and MR and relatively low for FR. Milling quality traits were significantly correlated with seed shape (seed diameter and seed plumpness). Multiple QTLs for milling traits were detected in six of seven linkage groups (LGs). The most stable QTLs governing DE and MR were clustered on LGs 1, 2, 3, and 7, whereas FR QTLs were clustered on LGs 4, 5, 6, and 7. The molecular markers identified for these traits could be used for improving milling quality in lentil breeding programs.
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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 | 0.001 | 0.001 |
| 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".