Genetic effects of marine stock enhancement: a case study based on the highly piscivorous Japanese Spanish mackerel
Bibliographic record
Abstract
We used a before–after control–impact design to quantify the genetic effects of the large piscivorous Japanese Spanish mackerel (Scomberomorus niphonius) stock enhancement program on wild populations in the Seto Inland Sea. Samples of 1424 wild and 230 hatchery fish collected from 13 sites around Japan were genotyped using five microsatellite markers. A total of 758 wild and 103 hatchery fish were sequenced for the mitochondrial DNA D-loop region. The population structure of Japanese Spanish mackerel was panmictic around Japan. Hatchery fish had significantly lower genetic diversity indices than did wild fish. However, there was no significant change in any of the diversity indices in the Seto Inland Sea, despite the substantial genetic mixing proportion of hatchery-origin genes (7.8%–14.5% from releases in 2001 and 2002), a conclusion supported by simulations. The estimated effective population size was surprisingly small (∼430–970) but stable in the Seto Inland Sea compared with the large census size. A Ryman–Laikre effect was not likely in the Japanese Spanish mackerel.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".