Extinction risk and bottlenecks in the conservation of charismatic marine species
Bibliographic record
Abstract
Abstract The oceans face a biodiversity crisis, but the degree and scale of extinction risk remains poorly characterized. Charismatic species are most likely to garner greatest support for conservation and thus provide a best‐case scenario of the status of marine biodiversity. We summarize extinction risk and diagnose impediments to successful conservation for 1,568 species in 16 families of marine animals in the movie Finding Nemo . Sixteen percent (12–34%) of those that have been evaluated are threatened, ranging from 9% (7–28%) of bony fishes to 100% (83–100%) of marine turtles. A lack of scientific knowledge impedes analysis of threat status for invertebrates, which have 1,000 times fewer conservation papers than do turtles. Legal protection is severely deficient for sharks and rays; only 8% of threatened species in our analysis are protected. Extinction risk among wide‐ranging taxa is higher than most terrestrial groups, suggesting a different conservation focus is required in the sea.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".