Effects of time-area closures on the distribution of snow crab fishing effort with respect to entanglement threat to North Atlantic right whales
Bibliographic record
Abstract
Abstract Time-area closures are increasingly used to mitigate cetacean entanglement by temporarily excluding fishing effort from areas where high densities of cetaceans and fishing overlap. The effort displaced by these closures can be redistributed to the areas that remain open, changing the distribution and density of fishing effort outside the closures. These patterns were evaluated for the southern Gulf of St. Lawrence snow crab fishery by comparing recent years (2015–2017) with 2018 when time-area closures were implemented to protect North Atlantic right whales. A predictive model framework was created to test how well we could predict the response of fishers to closures. Approximately 29% of the total fishing effort was displaced by the 2018 closures, increasing effort density outside the closures by 41%. Displaced fishing effort shifted farther from the closures than predicted, into areas which, prior to 2018, had low effort density, producing a higher threat of entanglement in these new areas. Fishing effort in 2018 remained as high as 2017, despite a lower quota and reduced trap limit. Consequently, the resulting effects of time-area closures on fishing patterns outside of the closures cannot be discounted if entanglement threat to whales is to be successfully mitigated.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".