Evidence for reduced genetic variation in severely deformed juvenile salmonids
Bibliographic record
Abstract
Inbreeding is one of the factors that can result in high infant mortality. In many species, however, mortality at early life stages is very difficult to observe in the wild and estimates from later life stages may therefore underestimate the effects of reduced genetic variability on fitness. Using microsatellites, we compared the amount of genetic variation in severely deformed fry of Lake Saimaa salmon (Atlantic salmon, Salmo salar) and brown trout (Salmo trutta) hatchery brood stocks with normal fry from the same brood stocks. We observed significantly lower standardized heterozygosity and higher internal relatedness values for deformed fry of both species compared with normal fry. Our results suggest that (i) inbreeding can be an important factor causing severe deformities in juvenile salmonids and (ii) high mortality of severely deformed low heterozygosity individuals in early life stages may partly explain why some studies exploring heterozygosity–fitness correlations fail to find any association. An important direction for future research on this topic will be to compare the results of experiments conducted in captivity with those of similar experiments conducted in wild or semi-wild conditions, as such studies would help to better understand the direct relevance of research conducted in captivity with respect to the conservation of wild 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".