Incidental Capture of Pinnipeds
Bibliographic record
Abstract
We reviewed the literature on incidental catches of pinnipeds by commercial fisheries using both passive and active fishing gear. Few incidental catch data were available for most species, although a substantial amount of information has recently become available for species in the North Pacific Ocean and the Northwest Atlantic Ocean off Eastern Canada. Incidental catches in passive gear appear to be of sufficient magnitude to have contributed to population declines of northern fur seals (Callorhinus ursinus) and Kuril seals (Phoca vitulina stejnegeri) in the North Pacific and harp seals (P. groenlandica) from the Barents Sea. Incidental catches in active gear appear at least partially responsible for the decline of northern sea lions (Eumetopias jubatus) in the North Pacific. Detrimental impacts of incidental catches are also indicated for New Zealand sea lions (Phocarctos hookeri) off the Auckland Islands, harbour seals (P. vitulina concolor) off Newfoundland and Alaska, grey seals (Halichoerus grypus) in the eastern Baltic and for endangered Mediterranean (Monachus monachus) and Hawaiian (M. schauinslandi) monk seals. Several factors appear to influence incidental catches of pinnipeds, including behavioural traits of individual species, age of individuals, fishing gear type, and the temporal and spatial overlap of a species' range with fishing activities. More and better data on incidental catches of marine mammals (pinnipeds and cetaceans) and sea-birds by individual fisheries are required in order to evaluate properly the magnitude of the problems and their potential impact on specific populations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".