Digest: Trait-dependent diversification and its alternatives
Bibliographic record
Abstract
Species selection—variation in diversification rates associated with variation in species traits—was once a fringe idea, at least among population biologists. But following the development of a novel suite of phylogenetic comparative methods (e.g., BiSSE; Maddison et al. ), comparative biologists went looking for evidence of species selection and found it, seemingly everywhere. However, recent studies have shown that state‐dependent speciation and extinction models (SSE) were prone to detecting associations between diversification rates with phenotypic traits under a variety of situations where the diversification rates were simulated completely independently of the traits (Maddison and FitzJohn ; Rabosky and Goldberg ). These findings led to a crisis in the field; it was unclear which previous discoveries were actually discoveries, and which were false positives. In this issue, Rabosky and Goldberg () develop an approach, which they call FiSSE that is less likely to find spurious associations between binary traits and diversification rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".