Fishing the line: catch and effort distribution around the seasonal haddock (<i>Melanogrammus aeglefinus</i>) spawning closure on the Scotian Shelf
Bibliographic record
Abstract
Permanent and seasonal area closures are a common regulatory strategy in multispecies fisheries; however, few studies have closely examined seasonal closures. We examined the impact of the Browns Bank spawning closure on the spatial distribution of fishing effort and how the fleet utilized a “fishing the line” strategy. Generalized estimating equations were used to examine changes in effort distribution when the closure was and was not in effect. Effort displaced from the bank concentrated primarily within two areas up to 30 km from the closure boundary, one along the east boundary line and one along the west. Trends in catch rate (as value) with distance from the line were further examined using generalized additive models during the closed period, with results differing between regions. In the east, areas of greater catch rate could be identified and typically corresponded to areas of greater effort, while in the west region, no trends in catch rates were often observed, potentially indicating vessel distributions that correspond to the ideal free distribution. Implementation of a seasonal area closure on Browns Bank resulted in concentrations of vessels near the closure boundary, suggestive of a fishing the line strategy, with specific catch rate trends depending on vessel spatial distributions and target species.
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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 | 0.000 | 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 | 0.000 | 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 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".