Treading lightly: Quantitative estimates of seafloor contact for longline trap and hook fishing gear
Bibliographic record
Abstract
Abstract Despite increasing calls for sustainability and ecosystem objectives to manage fishing gear interactions with bottom habitats there are few quantitative approaches for assessing risks from bottom contact fishing. Risk assessments for bottom longline fisheries are particularly challenging due to a lack of information for estimating bottom contact areas from longline gear. In this paper, we demonstrate how data sensors and video cameras deployed on fishing gear can be used to quantify the bottom contact area for longline trap and hook fishing gear from the British Columbia Sablefish fishery. Our bottom contact estimates indicate that Sablefish fishing risks to bottom habitat are low in the majority of fishing areas, since 91.8% of the area fished is expected to have had zero bottom contact over the last 17 years. For the other 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 can be expected to have a minimum of 17 years to recover between subsequent bottom contact events. We demonstrate an approach for estimating fisheries bottom contact that can be widely implemented across longline fisheries. Our findings address key data gaps in bottom impacts research for longline gear fisheries, allowing fishing risks to be quantified over fine spatial scales. Such quantitative approaches for habitat risk assessment can provide essential information for management decisions aimed at determining acceptable trade-offs between habitat preservation and fishery benefits.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".