Characteristics of the shark fisheries of Fiji
Bibliographic record
Abstract
Limited information is available on artisanal and subsistence shark fisheries across the Pacific. The aim of this study was to investigate Fiji's inshore fisheries which catch sharks. In January and February 2013, 253 semi-directive interviews were conducted in 117 villages and at local harbours on Viti Levu, Vanua Levu, Taveuni, Ovalau and a number of islands of the Mamanuca and Yasawa archipelagos. Of the 253 interviewees, 81.4% reported to presently catch sharks, and 17.4% declared that they did not presently catch any sharks. Of the 206 fishers that reported to catch sharks, 18.4% targeted sharks and 81.6% caught sharks as bycatch. When targeted, primary use of sharks was for consumption or for sale. Sharks caught as bycatch were frequently released (69.6%), consumed (64.9%) or shared amongst the community (26.8%). Fishers' identification based on an identification poster and DNA barcoding revealed that at least 12 species of elasmobranchs, 11 shark and one ray species (Rhynchobatus australiae) were caught. This study, which is the first focused exploration of the shark catch in Fiji's inshore fisheries, suggests that the country's artisanal shark fisheries are small but have the potential to develop into larger and possibly more targeted fisheries.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".