Methods to Reduce Sea Turtle Interactions in the Atlantic Canadian Pelagic Long Line Fleet
Bibliographic record
Abstract
Abstract This project investigates the role of fisheries management in the conservation of loggerhead ( Caretta caretta ) and leatherback sea turtles ( Dermochelys coriacea), both of which are currently listed as vulnerable by the IUCN (International Union for Conservation of Nature). These species migrate from nesting grounds in South America to feed on gelatinous zooplankton (jellyfish) in the North Atlantic off the coast of the United States and Canada. The seasonal foraging grounds of sea turtles heavily overlap with areas of high fishing effort for the longline tuna and swordfish fleet, a fishery that has significantly high rates of sea turtle incidents. The dynamic nature of sea turtle foraging patterns renders static spatio-temporal fishing area closures ineffective. Rather, turtle by-catch mitigation requires small-scale, event-triggered closures and decentralized management to reduce incidents while minimizing the negative socio-economic impact of area closures on fishermen. A number of methods that increase fishing selectivity have been implemented in other commercial fisheries around the globe and are suggested for the Atlantic Canadian fleet moving forward.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".