Estimation of Postrelease Longline Mortality in Pacific Halibut Using Acceleration-Logging Tags
Bibliographic record
Abstract
Abstract Pacific Halibut Hippoglossus stenolepis captured in directed commercial longline fisheries in Canada and the USA that are below the legal minimum size for retention must be returned to the sea without incurring additional injury. Estimates of mortality caused by discarding sublegal-sized fish are included in annual estimates of total mortality from all sources and affect the results of stock assessment and the yield available to fisheries. Currently, an average discard mortality rate (DMR) of 16% is applied to all sublegal-sized longline discards. These discards consist of fish that suffer injuries ranging from minor to severe. The 16% DMR that is currently applied was derived by averaging injury-specific DMRs that in turn assume 3.5% mortality of Pacific Halibut that are released to the sea with only minor injuries. The latter has been derived experimentally but only in captivity. Here, we used acceleration-logging pop-up archival transmitting tags to infer individual survival outcomes for Pacific Halibut that were released in situ following capture on longline gear. Postrelease behavioral data were evaluated for 75 fish that were at liberty for 2–96 d. Three fish were confidently inferred to have died after periods at liberty of 41–80 d, and another three fish may have died 96 d after release, resulting in minimum and maximum estimated 96-d postrelease DMRs of 4.2% (range = 0.0–8.7%) and 8.4% (range = 1.7–14.6%), respectively. These ranges are consistent with the currently applied value of 3.5%. However, the observation that no mortalities occurred until after 40 d postrelease departs from the findings of captive studies, in which the majority of capture-induced mortality occurred within 20 d of release.
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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".