Dissecting Vegetative Period Into Its Phenotypic and Genotypic Components Allows Environment‐Specific Breeding in Lentil ( <scp> <i>Lens culinaris</i> </scp> Medik)
Bibliographic record
Abstract
In legumes, flowering time is regulated by genes responsive to temperature and photoperiod, presenting challenges for high-latitude lentil producers who must adapt cultivars to short growing seasons and extended daylight hours. Therefore, prolonged vegetative periods are favored in those areas. To address this, we studied a recombinant inbred line (RIL) population, derived from a cross between the adapted cultivar CDC-Milestone and the non-adapted line ILL8006, to investigate phenology-related traits under long-day conditions in western Canada. Significant variation in days to emergence (DTE), days to flowering (DTF), and days to pod maturity (DTM) enabled analysis of the vegetative (VegP) and reproductive (RepP) periods within the population. We constructed a high-density genetic linkage map using molecular markers linked to genes in the Lcu.2RBY reference genome, identifying quantitative trait loci (QTLs) for those traits across four site-years in Saskatchewan. Differential expression analysis of known flowering time genes enhanced interpretation of the QTL results for flowering time. Three major DTE QTLs (qDTE2/3.II, qDTE2/3.III, and qDTE2/3.IV) on chromosome 2 explained 16%-28% phenotypic variability, depending on the environment, with in silico analysis identifying six curated genes as putative candidates within that region. A key DTF QTL (qDTF6.I) on chromosome 6 accounted for 23%-56% of phenotypic variability, harboring a homolog of the FLOWERING LOCUS T gene, whose role was explored alongside other candidate genes. Dissecting the vegetative period into DTE and DTF revealed distinct genetic controls for each trait, enabling breeders to combine early or late emergence and flowering to optimize adaptation and yield in diverse agroclimatic conditions.
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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".