The effects of high rearing density on the potential for domestication selection in hatchery culture of steelhead (<i>Oncorhynchus mykiss</i>)
Bibliographic record
Abstract
Hatchery-reared steelhead (Oncorhynchus mykiss) often have lower fitness than natural-origin fish when spawning in the wild. Fitness loss in hatcheries is partly due to genetic adaptation to captivity (domestication), but the underlying selection pressures driving adaptation remain unknown. Circumstantial evidence suggests that adaptation to hatcheries is accelerated when fish are reared at high density. We hypothesized two mechanisms by which high rearing densities could accelerate adaptation to the hatchery. First, high density could increase the among-family component of variation in fork length, which could increase the opportunity for selection after release. Second, a growth trade-off in fork length among families could occur across densities (family-by-environment interaction). We raised the same set of families, in replicate, at each of two densities. We found main effects of density (high density reduced body size) and family (accounted for 33%–53% of variance in size at release) on juvenile fork length. However, high density did not increase the percentage of variance in fork length among families, and there was weak evidence for a family-by-environment interaction. We propose an alternate model of how increased density might exacerbate domestication selection. The relationship between size at release and probability of survival is strongly nonlinear (almost truncational) for steelhead. Because high density decreases the fork lengths of all families approximately equally, high density could simply reduce the number of families that are above a threshold for high survival, resulting in strong among-family selection after release from the hatchery.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".