Quantitative estimates of contact with seafloor habitats by longline trap and hook fishing gear
Bibliographic record
Abstract
Abstract Despite increasing calls for sustainability and ecosystem objectives to manage fishing gear interactions with benthic habitat there are few quantitative approaches for assessing risks from bottom fisheries. Risk assessments for bottom longline fisheries are particularly challenging due to a lack of information for estimating habitat contact from longline gear. In this paper, we demonstrate how data sensors and video cameras deployed on fishing gear can be used to quantify habitat–contact area for sensitive benthic taxa (corals, sponges, and sea whips) from bottom longline trap and hook gear used by the British Columbia Sablefish (Anoplopoma fimbria) fishery. Our habitat–contact estimates indicate that Sablefish fishing gear has had zero contact with sensitive benthic habitats in 91.8% of the area fished over the last 17 years. For habitats within 8.2% of Sablefish fishing areas that experience some contact from fishing gear, the majority are only contacted once. This indicates that most habitats contacted by Sablefish gear are expected to have a minimum of 17 years to recover between subsequent gear contact. We demonstrate an approach for estimating habitat–contact area from fishing gear that can be widely implemented across longline fisheries, addressing a key data gap in in bottom-impacts research. Our habitat–contact estimates provide essential information for risk assessments that can inform management decisions on the acceptable trade-offs between habitat preservation and fishery benefits over fine spatial scales.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".