Trade-offs in the adaptation towards hatchery and natural conditions drive survival, migration, and angling vulnerability in a territorial fish in the wild
Bibliographic record
Abstract
Hatchery fish that support capture fisheries need to thrive in both hatchery and natural environments. We conducted joint experiments in both environments with individuals stemming from multiple generations held in captivity to test the performance of hatchery-reared ayu (Plecoglossus altivelis). Ayu is an annual, herbivorous, territorial, and amphidromous riverine fish native to Japan of high importance to recreational fisheries. Hatchery fish of the first hatchery generation exhibited poor growth and highest malformation rates relative to the second and following hatchery generations. The first generation offspring stocked into a natural stream also showed low survival and poor vulnerability to angling, suggesting that maladaptation to the hatchery environment explained the performance in the wild. By contrast, offspring of the seventh to ninth generations exhibited high growth in the hatchery environment, but when stocked into the wild they also exhibited low survival, maladapted migratory behaviour, and again poor vulnerability to angling. Consequently, intermediate generations held in captivity were found to offer the best fisheries performance and can thus be recommended for enhancements to support recreational fisheries.
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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 | 0.000 | 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".