Incidental capture of leatherback sea turtles in fixed fishing gear off Atlantic Canada
Bibliographic record
Abstract
Abstract Incidental capture in commercial fishing gear is a threat to many populations of marine megafauna, including sea turtles. While research has largely focused on pelagic longline impacts on sea turtles, fixed‐gear fisheries are a significant, historically understudied source of injury and mortality. The present study assesses the interaction of endangered leatherback sea turtles ( Dermochelys coriacea ) with fixed‐gear fisheries in high‐latitude seasonal foraging habitat where sub‐adult and adult turtles aggregate. Records of leatherback‐fishery interactions ( n = 205) were compiled from databases of publicly‐reported sea turtle sightings in Atlantic Canada (1998–2014) to identify the spatio‐temporal distribution of these events; to identify corresponding fisheries and gear types; and to describe the mechanics and outcomes of entanglements in fixed gear. Most reports came from coastal Nova Scotia ( n = 136) and Newfoundland ( n = 40), with reporting rates peaking in the mid‐to‐late 2000s. The majority of entanglements were reported during the summer months of July and August when leatherbacks are seasonally resident and several fisheries are active in continental shelf waters. Entanglements were most commonly reported in pot gear (e.g . snow crab, lobster, whelk) and trap nets (e.g . mackerel), reflecting extensive use of polypropylene lines distributed in the upper water column where leatherback foraging activity is concentrated. Given reporting biases and uncertainty regarding post‐release survivorship, entanglement mortalities should be considered a gross underestimate of true mortality rates. This study highlights both the importance of looking beyond pelagic longlines to evaluate leatherback interactions with fixed‐gear fisheries in high‐use continental shelf foraging habitat, and of involving the fishing industry in developing mitigation measures to reduce entanglement rates and associated turtle mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".