Bibliographic record
Abstract
<p>Since 1970, the global abundance of sharks has declined by 71% owing to an 18-fold increase in fishing pressure. The number of sharks caught as bycatch now exceeds the number of targeted catches, though industrial uses of bycatch are unknown. Trade routes for shark fins and meat are relatively well documented, but little is known about shark liver oil — the richest natural source for squalene — principally used in cosmetics. Endpoint market monitoring is crucial for improving our understanding of the shark trade. Stable isotope analysis of ẟ C using gas chromatography - combustion - isotope ratio mass spectrometry (GC-C-IRMS) can successfully determine squalene's source in finished cosmetic samples of mixed squalene origin. Results show that 26% of cosmetics contain shark-derived squalane, including samples carrying vegan or plant-based squalane claims. This research points to the urgent need for raw materials testing and the acquisition of species-specific data to eliminate this commercial threat to sharks. </p>
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.
How this classification was reachedexpand
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".