In hot soup: sharks captured in Ecuador's waters
Bibliographic record
Abstract
Sharks never stop growing and neither does the Asian demand for sharkfin soup. Ecuador is one nation of many that feeds the demand for fins, and fishers there catch more than 40 different shark species. But shark catches have been considerably underreported worldwide. Until the 2005 update of fisheries data, the United Nations Food and Agriculture Organisation (FAO) did not report elasmobranches for Ecuador, indicating that the Ecuadorian government did not report on these species. This study reconstructs Ecuador's mainland shark landings from the bottom up from 1979 to 2004. Over this period, shark landings for the Ecuadorian mainland were an estimated 7000 tonnes per year, or nearly half a million sharks. Reconstructed shark landings were about 3.6 times greater than those retroactively reported by FAO from 1991 to 2004. The discrepancies in data require immediate implementation of the measures Ecuadorian law mandates: eliminating targeted shark captures, finning and transshipments, as well as adoption of measures to minimise incidental capture. Most of all, a serious shark landings monitoring system and effective chain of custody standards are needed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".