Protecting rockfish through gear design: development of a selective flatfish trawl for the U.S. west coast bottom trawl fishery
Bibliographic record
Abstract
This study evaluated the potential to reduce the bycatch of rockfish (Sebastes spp.) in a flatfish trawl fishery by developing a selective trawl that separates flatfish from rockfish using differences in their behavior as the trawl net approaches. Using an alternate haul, randomized block design, we compared catches between a commonly used combination trawl and a newly designed, low-rise trawl incorporating a "cutback" headrope. For most of the target flatfish species, the experimental trawl had a 2559% higher catch rate (kilograms) than the control net (P < 0.05), consistent with greater footrope length in the experimental trawl. Increased catches of some small rockfish, skates, and sablefish (Anoplopoma fimbria) were also observed (P < 0.05). The catches of many larger roundfish and rockfish species, including canary rockfish (Sebastes pinniger), redstripe rockfish (Sebastes proriger), Pacific hake (Merluccius productus), and shortspine thornyhead (Sebastolobus alascanus) more than 25 cm long, were reduced from 34 to 97% in the experimental trawl (P < 0.05). Under weak-stock management conditions, in which the bycatch of overfished rockfish species limits directed flatfish fisheries, trawl designs that permit nontarget species to escape before entrainment can significantly reduce bycatch mortality and prevent premature closures of directed flatfish fisheries.
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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 | 0.001 | 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".