Monitoring extinction risk and threats of the world’s fishes based on the Sampled Red List Index
Bibliographic record
Abstract
Abstract Global biodiversitytargets require us to identify species at risk of extinction and quantify status and trends of biodiversity. The Red List Index (RLI) tracks trends in the conservation status of entire species groups over time by monitoring changes in categories assigned to species. Here, we calculate this index for the world’s fishes in 2010, using a sampled approach to the RLI based on a randomly selected sample of 1,500 species, and also present RLI splits for freshwater and marine systems separately. We further compare specific traits of a worldwide fish list to our sample to assess its representativeness. Overall, 15.1% of species in the sample were estimated to be threatened with extinction, resulting in a sampled RLI of 0.914 for all species, 0.968 in marine and 0.862 in freshwater ecosystems. Our sample showed fishing as the principal threat for marine species, and pollution by agricultural and forestry effluents for freshwater fishes. The sampled list provides a robust representation for tracking trends in the conservation status of the world’s fishes, including disaggregated sampled indices for marine and freshwater fish. Reassessment and backcasting of this index is urgent to check the achievement of the commitments proposed in global biodiversity targets.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".