Signatures of Random and Selective Mass Extinctions in Phylogenetic Tree Balance
Bibliographic record
Abstract
Current models of diversification with evolving speciation rates have trouble mimicking the extreme imbalance seen in estimated phylogenies. However, these models have not incorporated extinction. Here, we report on a simple simulation model that includes heritable and evolving speciation rates coupled with mass extinctions, Random (but not selective) mass extinctions, coupled with evolving among-lineage variation in speciation rates, increase imbalance of postrecovery clades. Thus, random mass extinctions are plausible contributors to the imbalance of modern clades. Paleontological evidence suggests that mass extinctions are often random with respect to ecological and morphological traits, consistent with our simulations. In contrast, evidence that the current anthropogenic mass extinction is phylogenetically selective suggests that the current extinction episode may be qualitatively different from past ones in the way it reshapes future biotas.
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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".