Addressing bycatch of depleted species through a marine conservation network in the Canadian Atlantic
Bibliographic record
Abstract
Bycatch of depleted and vulnerable species is a pressing conservation issue that undermines the sustainability of fisheries worldwide. Here, we utilised spatiotemporal modelling of fisheries-independent survey data to evaluate the potential for existing and proposed Marine Conservation Areas (MCAs) in Atlantic Canada to reduce bycatch vulnerability for three severely depleted species—Atlantic cod ( Gadus morhua), American plaice ( Hippoglossoides platessoides), and white hake ( Urophycis tenuis)—commonly caught as bycatch in commercial trawl fisheries on the Scotian Shelf-Bay of Fundy. We overlaid predicted distributions of abundance for these depleted species with those of commercially targeted haddock ( Melanogrammus aeglefinus) and pollock ( Pollachius virens) to identify areas of high-vulnerability. Our analysis showed that a fully implemented MCA network would overlap with an average of 16% of high-vulnerability area for individual species and 20% when combined as a single group, an increase of 9% and 13%, respectively, from existing MCAs. This approach can be used more generally by employing readily available survey data to optimise both fisheries management and biodiversity objectives in marine conservation planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".