A trap with a twist: evaluating a bycatch reduction device to prevent rockfish capture in crustacean traps
Bibliographic record
Abstract
Abstract Favaro, B., Duff, S. D., and Côté, I. M. 2013. A trap with a twist: evaluating a bycatch reduction device to prevent rockfish capture in crustacean traps. – ICES Journal of Marine Science, 70:114–122. Bycatch, or the incidental capture of non-target species, occurs in fisheries around the world, with often detrimental ecological consequences. Bycatch reduction devices (BRDs) that increase catch specificity have been used successfully in some fisheries, and the development of such devices remains an important component of the global effort to reduce bycatch rates. We tested novel devices designed to exclude juvenile rockfish (Sebastes spp.) from traps used to catch spot prawns (Pandalus platyceros), a commercially important species in British Columbia, Canada. The devices included reductions in trap opening sizes and novel bent-tunnel openings. Reducing trap opening size did not affect bycatch rates of rockfish or other non-target fish species. In contrast, bent-tunnel BRDs eliminated rockfish bycatch, and two of the bent-tunnel variants also excluded other fish species. However, prawn catch rates were reduced in all modified gear, and large prawns were often excluded more than small prawns. Videos recorded in situ revealed that prawn attempts to enter traps took longer and were more likely to fail in BRD-equipped than in unmodified traps. We conclude that bent-tunnel BRDs have the potential to be useful, but improvements are needed to increase prawn catch to levels similar to that of unmodified traps.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".