Small-scale fishing has affected abundance and size distributions of deepwater snappers and groupers in the MesoAmerican region
Bibliographic record
Abstract
Abstract Deepwater fisheries in the Caribbean Sea are poorly studied and mostly unmanaged, despite their importance to local economies and food security. In the MesoAmerican region, deepwater fisheries are nearshore and easily accessible in many locations by small vessels, but historical and contemporary fishing effort varies by country. We used standardized fishery-independent methods, including vertical longlines and baited remote underwater video (BRUV) to assess the relative abundance and distribution of deepwater (100–550 m) snappers and groupers in Belize and Honduras. Fishery-dependent samples were used to supplement spatial distribution and body length data. Gathered data revealed that Belize, with a smaller fishing population and shorter history of deepwater fishing, had overall higher abundance of groupers and snappers and that fish were significantly larger than those in Honduras, which has a well-established and larger deepwater fishery. Water temperature was found to influence the abundance and occurrence of deepwater snappers more than depth. Deepwater snappers were widely-distributed throughout available habitat in both countries, but groupers were more patchy, and rarely encountered in the more heavily fished areas off Honduras. Our results suggest that a precautionary approach to fisheries management and targeted biological research of these stocks is required, which is particularly relevant for Belize, where climate change and overfishing mitigation measures are focused on an expansion of the deepwater fishery.
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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 | 0.001 |
| 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".