In hot soup: sharks captured in Ecuador's waters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 it