Evaluating catch and mitigating risk in a multispecies, tropical, inshore shark fishery within the Great Barrier Reef World Heritage Area
Bibliographic record
Abstract
Small-scale and artisanal fisheries for sharks exist in most inshore, tropical regions of the world. Although often important in terms of food security, their low value and inherent complexity provides an imposing hurdle to sustainable management. An observer survey of a small-scale commercial gill-net fishery operating within the Great Barrier Reef World Heritage area revealed at least 38 species of elasmobranch were present in the catch. Of the total elasmobranch catch, 95% was 25 species of Carcharhiniformes from the families Carcharhinidae, Hemigaleidae and Sphyrnidae. Individual species were captured in a variety of ways by the fishery, often with strongly biased sex ratios and in a variety of life stages (e.g. neonates, juveniles, adult). Despite this, the main carcharhiniform taxa captured could be qualitatively categorised into four groups based on similar catch characteristics, body size and similarities in life history: small coastal (<1000 mm); medium coastal (1000–2000 mm); large coastal/semi-pelagic (>2000 mm); and hammerheads. Such groupings can potentially be useful for simplifying management of complex multispecies fisheries. The idiosyncrasies of elasmobranch populations and how fisheries interact with them provide a challenge for management but, if properly understood, potentially offer underutilised options for designing management strategies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".