Decline in <scp>Atlantic</scp> wolffish <i>Anarhichas lupus</i> in the <scp>North Sea</scp>: Impacts of fishing pressure and climate change
Bibliographic record
Abstract
Anarhichas lupus is a boreo-Arctic species with biological characteristics often associated with vulnerability to overexploitation. Although not commercially targeted in the North Sea, A. lupus is a bycatch species in mixed demersal fisheries. Here we provide an overview of the status of A. lupus in the North Sea, as observed from commercial landings and fishery-independent trawl survey data. A. lupus was once common across much of the central and northern North Sea but, since the 1980s, have declined in abundance, demographic characteristics (reduced size) and geographical range, with the shallower and more southerly parts of its range most impacted. A. lupus is still relatively frequent in the northern North Sea, where fishing intensity, though decreasing, is high. Bycatch through fishing remains a potential threat and, considering the likely impacts of predicted climate change on cold-water species, risks of further regional depletion and/or range contraction remain. Whether or not A. lupus is able to re-establish viable populations in former habitat in UK coastal waters is unknown. Given the lack of data, the precautionary principle would suggest that manageable pressures be minimized where the species and its habitat are at risk of further impacts, and more regular assessments of population status be undertaken.
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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) | 0.002 | 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".