Measuring trends in extinction risk: a review of two decades of development and application of the Red List Index
Bibliographic record
Abstract
The Red List Index (RLI) is an indicator of the average extinction risk of groups of species and reflects trends in this through time. It is calculated from the number of species in each category on the IUCN Red List of Threatened Species, with trends influenced by the number moving between categories when reassessed owing to genuine improvement or deterioration in status. The global RLI is aggregated across multiple taxonomic groups and can be disaggregated to show trends for subsets of species (e.g. migratory species), or driven by particular factors (e.g. international trade). National RLIs have been generated through either repeated assessments of national extinction risk in each country or through disaggregating the global index and weighting each species by the proportion of its range in each country. The RLI has achieved wide policy uptake, including by the Convention on Biological Diversity and the United Nations Sustainable Development Goals. Future priorities include expanding its taxonomic coverage, applying the RLI to the goals and targets of the Kunming-Montreal Global Biodiversity Framework, incorporating uncertainty in the underlying Red List assessments, integrating into national RLIs the impact of a country on species' extinction risk abroad, and improving analysis of the factors driving trends.This article is part of the discussion theme issue 'Bending the curve towards nature recovery: building on Georgina Mace's legacy for a biodiverse future'.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".