Loss of Large Predatory Sharks from the Mediterranean Sea
Bibliographic record
Abstract
Evidence for severe declines in large predatory fishes is increasing around the world. Because of its long history of intense fishing, the Mediterranean Sea offers a unique perspective on fish population declines over historical timescales. We used a diverse set of records dating back to the early 19th and mid 20th century to reconstruct long-term population trends of large predatory sharks in the northwestern Mediterranean Sea. We compiled 9 time series of abundance indices from commercial and recreational fishery landings, scientific surveys, and sighting records. Generalized linear models were used to extract instantaneous rates of change from each data set, and a meta-analysis was conducted to compare population trends. Only 5 of the 20 species we considered had sufficient records for analysis. Hammerhead (Sphyrna spp.), blue (Prionace glauca), mackerel (Isurus oxyrinchus and Lamna nasus), and thresher sharks (Alopias vulpinus) declined between 96 and 99.99% relative to their former abundance. According to World Conservation Union (IUCN) criteria, these species would be considered critically endangered. So far, the lack of quantitative population assessments has impeded shark conservation in the Mediterranean Sea. Our study fills this critical information gap, suggesting that current levels of exploitation put large sharks at risk of extinction in the Mediterranean Sea. Possible ecosystem effects of these losses involve a disruption of top-down control and a release of midlevel consumers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".