Extinction risk and conservation of critically endangered angel sharks in the Eastern Atlantic and Mediterranean Sea
Bibliographic record
Abstract
Abstract Understanding the details of local and regional extinctions allows for more efficient allocation of conservation activities and resources. This involves identifying where populations persist, where populations may still be present, and where populations may be locally extinct. Three threatened angel sharks occur in the Eastern Atlantic and Mediterranean Sea: Sawback Angelshark (Squatina aculeata), Smoothback Angelshark (Squatina oculata), and Angelshark (Squatina squatina). Population sizes and geographic ranges of these species have been reduced due to overfishing and habitat loss, placing them among the world s most threatened chondrichthyans. We revise distribution maps, review global status, and present a Conservation Strategy to protect and restore these angel shark populations by minimizing fishing mortality, protecting critical habitat, and mitigating human disturbance. Updated distributions reveal that a halving of the geographic extent may have occurred for all three species, with potential declines of 51% for Sawback Angelshark, 48% for Smoothback Angelshark, and 58% for Angelshark. While 20 national and international management measures are now in place for Angelshark, only half of these include the other two species. We encourage further conservation action to adopt and develop this Conservation Strategy to restore angel shark populations to robust levels and safeguard them throughout their range.
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 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".