Evaluation of collaboratively developed management measures to reduce coral and sponge bycatch in a fully monitored multispecies trawl fishery
Bibliographic record
Abstract
To protect cold-water corals and sponges from fishing damage, management changes were made in 2012 to the groundfish bottom trawl fishery British Columbia, Canada. The Groundfish Trawl Habitat Agreement restricted the spatial footprint of the fishery and introduced a cold-water coral and sponge bycatch quota, which was among the world’s first. Using 12 years of catch records from the fishery, we found a 31% decrease in overall frequency of encounters of cold-water coral and sponge, a 76% decrease in mean catch weight, and an 89% decrease in total annual catch. We tracked changes in the relative utilization of fine-scale fishing grounds (“fishing opportunities”) and found evidence of active avoidance of areas with high cold-water coral and sponge density. The habitat agreement appears overall to have been successful at reducing impacts to cold-water coral and sponge, although we identified several areas of potential conservation concern where effort and catch have not decreased. Nonspatial management measures in a complex multispecies fishery can result in spatial changes in fishing behaviour, with positive conservation outcomes for bycatch species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".