Evaluation of low breaking‐strength and acoustic on‐demand fishing gear to mitigate entanglements of North Atlantic right whales in the southern Gulf of St. Lawrence snow crab fishery
Bibliographic record
Abstract
Finding methods to mitigate the risk and severity of North Atlantic right whale entanglements in fishing gear is increasingly urgent as these endangered whales use the southern Gulf of St. Lawrence (sGSL) as a summer feeding ground. In collaboration with snow crab fishers in the sGSL, we measured the tension of buoy lines while hauling traps, field tested the hauling of low breaking‐strength gear, and explored the use of on‐demand gear to avoid deploying vertical buoy lines. With conventional buoy lines, 40% of the hauling events reached maximum tensions of 7.56 kN (kiloNewtons; 1,700 lbf) or more during regular fishing operations. When evaluating methods to reduce the tension of buoy lines while lifting single traps, we found that the use of a hydraulic tension limiter to maintain low tension was effective and relatively easy to retro‐fit to existing vessels. Four types of weak gear configurations were tested in sea trials using a hydraulic load tension limiter: (1) buoy lines consisting of weak rope throughout; (2) buoy lines consisting of regular rope with inserts of either weak rope, (3) breakaway sleeves or (4) links, with the only gear modification parting being sleeves. The tension needed to break weak rope reduced after being used to lift a trap six times. During the 2022 fishing season, 300 trawls (strings of multiple traps) were hauled by 19 fishers using on‐demand gear in areas closed to conventional buoyed line fishing gear, with a total catch of > 203 tonnes, representing the first commercial application of this technology in Canadian waters. While 94% of the deployed sets were retrieved without grappling, only 65% of them were recovered after sending a single trigger signal from a mobile application. For this technology to be widely implemented, the trigger success rate must be improved. We measured elevation profiles of model groundlines with different proportions of sinking and positively buoyant rope and proportions of excess length at different current speeds. Our results support the use of sinking groundline to achieve low arc heights above the seafloor for on‐demand fishing using trawls. We believe that the collaborative framework applied in this study was effective in engaging fishers to apply innovative practices to reduce the risk and severity of right whale entanglements with snow crab fishing gear in the sGSL. The feedback provided by fishers helped define our methods and ensure their practical application. Moreover, this initiative strengthened international synergies, fostering collaboration between scientists, industry stakeholders and regulatory bodies to accelerate the adoption of whale‐safe fishing practices on a broader scale.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".