FAST‐TRACK: Integrating QTL mapping and genome scans towards the characterization of candidate loci under parallel selection in the lake whitefish (<i>Coregonus clupeaformis</i>)
Bibliographic record
Abstract
As natural selection must act on underlying genetic variation, discovering the number and location of loci under the influence of selection is imperative towards understanding adaptive divergence in evolving populations. Studies employing genome scans have hypothesized that the action of divergent selection should reduce gene flow at the genomic locations implicated in adaptation and speciation among natural populations, yet once 'outlier' patterns of variation have been identified the function and role of such loci needs to be confirmed. We integrated adaptive QTL mapping and genomic scans among diverging sympatric pairs of the lake whitefish (Coregonus clupeaformis) species complex in order to test the hypothesis that differentiation between dwarf and normal ecotypes at growth-associated QTL was maintained by directional selection. We found evidence of significantly high levels of molecular divergence among eight growth QTL where two of the strongest candidate loci under the influence of directional selection exhibited parallel reductions of gene flow over multiple populations.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".