Discards, hooking, and post-release mortality of porbeagle (<i>Lamna nasus</i>), shortfin mako (<i>Isurus oxyrinchus</i>), and blue shark (<i>Prionace glauca</i>) in the Canadian pelagic longline fishery
Bibliographic record
Abstract
Global discards of sharks greatly exceed reported landings, yet there are few estimates of mortality after release. Based on more than 21 000 fisheries observer records and the results of 109 popup satellite archival tags, all sources of fishing-induced mortality (harvest, capture, and post-release) were estimated for blue sharks ( Prionace glauca ), shortfin mako ( Isurus oxyrinchus ), and porbeagle ( Lamna nasus ) in the Canadian pelagic longline fishery between 2010 and 2014. Hooking mortality ranged from 15 to 44%, with porbeagles and makos experiencing much greater mortality than blue sharks. The post-release mortality rate varied between 10 and 31%, with porbeagle and mako again having the highest mortality rate. Overall, about one-half of the hooked porbeagles and makos died during or after fishing, with most of the post-release mortality occurring within 2 d of release. Landed catch accounted for less mortality in porbeagle and blue sharks than did the combination of hooking and post-release mortality. These results indicate that the conservation benefits of mandatory release regulations for pelagic longline gear are not nearly as great as is now assumed.
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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 | 0.001 | 0.001 |
| 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".