A global indicator of species recovery
Bibliographic record
Abstract
Monitoring progress toward meeting global biodiversity goals involves several indicators, including, at the species level, the International Union for Conservation of Nature (IUCN) Red List Index (RLI) and the Living Planet Index (LPI). However, at present, there is no indicator specifically for tracking species recovery, despite this being enshrined in the mission of the Convention on Biological Diversity's Kunming-Montreal Global Biodiversity Framework (GBF). The IUCN recently adopted the Green Status of Species (GSS), a global standard for measuring species recovery and for assessing the role played by conservation in species recovery. An index based on GSS has been adopted as an indicator for multiple elements of GBF. However, a methodology underpinning the index itself has not previously been published or elaborated. We have therefore developed the Green Status Index of Species Recovery (GSI) for use as a global indicator of progress toward species recovery. We devised GSI to reflect the uncertainties of the underlying GSS assessments and developed methods to disaggregate its global value to reflect the contribution of each country to the recovery of the species within its borders. Overall, we designed the GSI to exhibit key attributes of an effective global indicator, including an explicit objective aligned with global biodiversity goals and a sound methodological basis. The GSI complements existing indicators, such as RLI and LPI, because it fills an important niche in measuring biodiversity trends, going beyond extinction risk and population abundance. As a test, we applied the GSI to a set of species and found that these species were less than halfway to full recovery and moved farther away from full recovery since the mid-20th century. Although the deployment of GSI for complete taxonomic groups will require a considerable scaling up of effort, a sampled approach is feasible and can be operational by 2030.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".