Genetic evaluation of Atlantic salmon for growth traits incorporating <scp>SNP</scp> markers
Bibliographic record
Abstract
Genetic evaluations of individual fish were calculated for growth traits in North American Atlantic salmon with and without inclusion of genetic markers. The number of SNP markers was reduced to 6,000 and further to 270 in order to reduce the problem of overparameterization. SNP genotypes were predicted for all ungenotyped animals in the pedigree. Analysis of traits used a model with polygenic effects and SNP markers together. Polygenic effects refer to the additive genetic effects that remain after accounting for SNP genotypes. SNP marker genotypes were included as covariates to evaluate fish for growth traits (weight and length) in different environments (freshwater and seawater) with genders separated. Including regressions on SNP marker genotypes reduced the sum of squares of residuals by 2.7%-12.5% and increased the variability of Mendelian sampling effects (i.e., within-family variation) compared to traditional animal model evaluations. Genetic evaluations may be carried out with a few hundred markers which may be more affordable for genotyping large numbers of fish.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".