Survival outcome patterns revealed by deploying advanced tags in quantity: Pacific halibut (<i>Hippoglossus stenolepis</i>) survivals after release from trawl catches through expedited sorting
Bibliographic record
Abstract
Bycatch of Pacific halibut (Hippoglossus stenolepis) limits many trawl fisheries in Alaska and greatly concerns stakeholders from local communities and fisheries that rely on Pacific halibut. To reduce Pacific halibut mortality, trawlers in the Bering Sea that target flatfish have been developing expedited release procedures to sort Pacific halibut from catches earlier than current regulations allow, while continuing accurate bycatch accounting. We studied survival rates of released Pacific halibut from three trawlers by deploying accelerometer-equipped pop-up satellite archival tags (PSATs) on 160 fish handled under expedited procedures. PSATs recorded and transmitted two metrics indicating swimming activity every 2 h while attached to the fish (for up to 60 days). Analysis of the resulting survival outcomes largely validated current survival-estimation methods, based on structured viability assessments, and found that longer fish length, shorter duration of air exposure, and shorter duration of trawl tow improved predicted Pacific halibut survival. Differences in these results were detected among vessel trips and species targeted by trawling. PSATs provided detailed data from nearly all tagged fish, while exposing fish to conditions experienced by normal releases.
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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 | 0.000 |
| 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) | 0.001 | 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".