Genetic mapping of quantitative trait loci influencing growth, development and morphology in Atlantic salmon (Salmo salar, L.)
Bibliographic record
Abstract
The molecular basis for heritable variation in quantitative traits is poorly understood in most species of fish. This is the case even for salmonids such as the Atlantic salmon (Salmo salar L.), one the most intensively studied and economically important aquaculture species. Quantitative genetic studies tell us that traits such as growth, development and morphology are likely to be controlled by a large number of genes, but to date there have only been two loci, the allozyme loci MEP-2* and TRP-2*, which have been shown to consistently influence the salmon phenotype. This thesis sets out to increase understanding of the molecular basis of phenotypic variation in growth performance, development and morphology in the Atlantic salmon. Experiments were performed to look for associations of phenotypic variation with genotypic variation at the molecular level and, thereby, to identify molecular markers linked to regions of the salmon genome containing genes influencing these quantitative traits. This was achieved by analysing phenotypic performance in two F2 backcross families derived from crosses of two outbred wild salmon populations, one from Scotland and one from Canada, which are divergent with respect to growth, development and morphology. The study began with an examination of the relationship between growth prior to first-feeding, utilising endogenous energy supplies, and growth post first-feeding, utilising exogenous energy supplies. The study then focused on growth, development and morphology in individually fish post first-feeding.
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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) | 0.000 | 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".