Potential for managing life history diversity in a commercially exploited intermediate predator, the goldsinny wrasse (Ctenolabrus rupestris)
Bibliographic record
Abstract
Abstract Small-bodied wrasse species are important for structuring coastal marine ecosystems but are also increasingly harvested as parasite cleaners on farmed salmon. Identifying management regulations that will support long-term sustainability of wrasse fisheries is challenging, because there is still limited knowledge about the impacts of fisheries on the demography of these intermediate predators in their natural environments. To this end, we studied individual growth histories of goldsinny wrasse (Ctenolabrus rupestris) at a fine spatial scale across replicated marine protected areas (MPAs) and areas open to commercial harvesting on the Norwegian coast. The MPAs were established 1–7 years prior to our sampling. We detected significant fine-scale spatial variation in wrasse asymptotic body size, but found no consistent difference between MPAs and fished areas. Male wrasses reached larger asymptotic body sizes than females, whereas fyke nets captured individuals with larger asymptotic body sizes compared with baited traps. These are the two commonly used gear types in wrasse fisheries. An extended use of baited traps, along with slot-size limits, could therefore aid in protecting large-growing phenotypes such as nest-guarding males.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".