Early Life History Variation among Hatchery‐ and Wild‐Origin Lake Trout Reared in a Hatchery Environment
Bibliographic record
Abstract
Abstract Hatcheries play a key role in augmenting populations for conservation, harvest, or both, although rapid domestication and adaptation to hatchery conditions may lead to fish that are maladapted to natural environments. Three processes may lead to domestication: (1) negative selection against fish adapted to wild environments, (2) positive selection for fish that thrive in artificial conditions, or (3) relaxation of selection pressures. In this study, early life history traits and survival were contrasted among wild‐ and hatchery‐origin lake trout Salvelinus namaycush reared in a common hatchery environment to address the hypotheses of processes leading to domestication. Examination of egg size, egg survival, fry deformity, and early growth rate found no evidence of negative selection against wild‐origin lake trout or positive selection for hatchery‐origin lake trout when reared in a hatchery environment. Wild‐origin lake trout outperformed hatchery‐origin fish in all life history traits examined, suggesting that relaxation of natural selective pressures may be occurring in the hatchery environment. Furthermore, the hatchery‐origin strains failed to show maternal effects on egg size and exhibited limited variability in egg size and hatching time, as well as no significant differences in early growth rate, suggesting potential homogenization of life history traits as a result of the hatchery environment.
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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 | 0.000 | 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) | 0.001 | 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".