Unnatural random mating policies select for younger age at maturity in hatchery Chinook salmon (Oncorhynchus tshawytscha) populations
Bibliographic record
Abstract
We explored the long-term consequences of three mating regimes ((1) completely random, (2) completely random but excluding jacks (age 2 males), and (3) male length ≥ female length) on age and sex structure of wild and hatchery populations of Chinook salmon ( Oncorhynchus tshawytscha ). Regimes similar to regimes 1 or 2 are used at most salmon hatcheries, whereas regime 3 emulates the outcomes of natural spawning behaviors that favor larger males. Inheritance of age at maturity is captured in age- and sex-structured models via matrices of age- and sex-specific conditional maturation probabilities that depend on age and sex of parents. In unexploited populations, regime 1 leads to substantial long-term selection for younger age at maturity, an effect that is somewhat reduced by regime 2, but greatly reduced under regime 3. Equilibrium age and sex structures for wild and hatchery populations under regime 3 are similar to those of natural populations, whereas mating regime 1 generates age structure that is greatly shifted toward younger ages and jacks. To prevent unintentional selection for younger age at maturity, we recommend that large-scale hatcheries replace unnatural completely random mating regimes with mating regimes that emulate the outcomes of natural spawning behaviors.
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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 | 0.002 |
| 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 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".