Mapping of quantitative trait loci associated with size, shape, and parr mark traits using first- and second-generation backcrosses between European and North American Atlantic salmon (<i>Salmo salar</i>)
Bibliographic record
Abstract
Little is known about the genetic architecture of traits important for salmonid restoration ecology. We mapped quantitative trait loci (QTL) using single nucleotide polymorphisms (SNPs) for juvenile body length, weight, shape, and vertical skin pigmentation patterns (parr marks) within three hybrid backcross families between European and North American subspecies of Atlantic salmon. Amounts of variation in skin colour and pattern quantified in the two second-generation transAtlantic families exceeded the ranges seen in purebred populations. GridQTL analyses using low-density female-specific linkage maps detected QTL showing experiment-wide significance on Ssa02, Ssa03, Ssa09, Ssa11, Ssa19, and Ssa26/28 for both length and weight; on Ssa04 and Ssa23 for parr mark number; on Ssa09 and Ssa13 for parr mark contrast; and on Ssa05, Ssa07, Ssa10, Ssa11, Ssa18, Ssa23, and Ssa26/28 for geometric morphometric shape coordinates. Pleiotrophic QTL on Ssa11 affected length, weight, and shape. No QTL was found that explained more than 10% of the phenotypic variance in pigmentation or shape traits. Each QTL was approximately positioned on the physical map of the Atlantic salmon genome. Some QTL locations confirmed previous studies but many were new. Studies like ours may increase the success of salmon restoration projects by enabling better phenotypic and genetic matching between introduced and extirpated strains.
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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 | 0.000 |
| 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".