Global primary predictors of extinction risk in primates
Bibliographic record
Abstract
Identifying the main predictors of species' extinction risk while accounting for the effects of spatial and phylogenetic structures in the data is key to preventing species loss in tropical forests through adequate conservation practices. We recorded 22 705 precise geographical locations of primate occurrence across four major geographic realms (Neotropics, mainland Africa, Madagascar and Asia) to assess predictors of threat status using a novel Bayesian spatio-phylogenetic approach. We estimated the relative contributions of fixed factors (forest amount, body mass, home range, diel activity, locomotion, evolutionary distinctiveness and climatic instability) and random factors (space and phylogeny) to primate extinction risk. Precipitation instability increased the extinction risk in the Neotropics but decreased it in mainland Africa and Madagascar. Forest amount was negatively associated with extinction risk in all realms except Madagascar. Body mass increased the extinction risk in the Neotropics and Madagascar, whereas home range increased the extinction risk in mainland Africa and decreased it in Asia. Evolutionary distinctiveness negatively influenced extinction risk only in mainland Africa. Our findings highlight the importance of climate change mitigation and forest protection strategies. Increasing the protection of large primates and reducing hunting are also essential.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".