Business partner or simple catch? The economic value of the sicklefin lemon shark in French Polynesia
Bibliographic record
Abstract
Most arguments invoked so far by the scientific community in favour of shark conservation rely on the ecological importance of sharks, and have little impact on management policies. During a 57-month study, we were able to individually recognise 39 sicklefin lemon sharks that support a shark-feeding ecotourism activity in Moorea Island, French Polynesia. We calculated the direct global revenue generated by the provisioning site, based on the expenses of local and international divers. The total yearly revenue was around USD5.4 million and the 13 sharks most often observed at the site had an average contribution each of around USD316 699. Any one of these sharks represents a potential contribution of USD2.64 million during its life span. We argue that publicising economic values per individual will be more effective than general declarations about their ecological importance for convincing policy makers and fishers that a live shark is more valuable than a dead shark for the local economy. Studies monitoring the potential negative ecological effects of long-term feeding of sharks should, however, be conducted to ensure these are also considered. Besides declarations about the non-consumptive direct-use value of sharks, as promoted by ecotourism, the calculation of their other economic values should also benefit shark conservation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".