The dynamics underlying avian extinction trajectories forecast a wave of extinctions
Bibliographic record
Abstract
Abstract Population decline is a process, yet estimates of current extinction rates often consider just the final step of that process by counting numbers of species lost in historical times. This neglects the increased extinction risk that affects a large proportion of species, and consequently underestimates the effective extinction rate. Here, we model observed trajectories through IUCN Red List extinction risk categories for all bird species globally over 28 years, and estimate an overall effective extinction rate of 2.17 × 10−4/species/year. This is six times higher than the rate of outright extinction since 1500, as a consequence of the large number of species whose status is deteriorating. We very conservatively estimate that global conservation efforts have reduced the effective extinction rate by 40%, but mostly through preventing critically endangered species from going extinct rather than by preventing species at low risk from moving into higher-risk categories. Our findings suggest that extinction risk in birds is accumulating much more than previously appreciated, but would be even greater without conservation efforts.
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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) | 0.000 | 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".