Revisiting the impacts of non-random extinction on the tree-of-life
Bibliographic record
Abstract
The tree-of-life represents the diversity of living organisms. Species extinction and the concomitant loss of branches from the tree-of-life is therefore a major conservation concern. There is increasing evidence indicating that extinction is phylogenetically non-random, such that if one species is vulnerable to extinction so too are its close relatives. However, the impact of non-random extinctions on the tree-of-life has been a matter of recent debate. Here, we combine simulations with empirical data on extinction risk in mammals. We demonstrate that phylogenetically clustered extinction leads to a disproportionate loss of branches from the tree-of-life, but that the loss of their summed lengths is indistinguishable from random extinction. We argue that under a speciational model of evolution, the number of branches lost might be of equal or greater consequences than the loss of summed branch lengths. We therefore suggest that the impact of non-random extinction on the tree-of-life may have been underestimated.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".