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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 it