Collaborative Pacific Halibut, Hippoglossus stenolepis, Bycatch Control by Canada and the United States
Bibliographic record
Abstract
ABSTRACT—Bycatch mortality of Pacific halibut, Hippoglossus stenolepis, in nontarget fisheries is composed primarily ofimmature fish, and substantial reductions in yield to directed halibut fisheries result from this bycatch. Distant-water bottomtrawl fleets operating off the North American coast, beginning in the mid 1960’s, experienced bycatch mortality of over 12,000 t annually. Substantial progress on reducing this bycatch was not achieved until the of extension fisheries jurisdictions by the United States and Canada in 1977. Bycatch began to increase again during the expansion of domestic catching capacity forgroundfish, and by the early 1990’s it had returned to levels seen during the period of foreign fishing. Collaborative action by Canada and the United States through the International Pacific Halibut Commission has resulted in substantial reductions in bycatch mortality in some areas. Methods of control have operated at global, fleet, and individual vessel levels. We evaluate thehierarchy of effectiveness for these control measures and identify regulatory needs for optimum effects. New monitoring technologies offer the promise of more cost-effective approaches to bycatch reduction.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".