Data from: The role of behavioural flexibility in primate diversification
Bibliographic record
Abstract
Identifying the factors that influence species diversification is fundamental to our understanding of the evolutionary processes underlying extant biodiversity. Behavioural innovation, coupled with the social transmission of new behaviours, has been proposed to increase rates of evolutionary diversification, as novel behaviours expose populations to new selective regimes. Thus, it is believed that behavioural flexibility may be important in driving evolutionary diversification across animals. We test this hypothesis within the primates, a taxonomic group with considerable among-lineage variation in both species diversity and behavioural flexibility. We employ a time cutoff in our phylogeny to help account for biases associated with recent taxonomic reclassifications and compare three alternative measures of diversification rate that consider different phylogenetic depths. We find that the presence of behavioural innovation and social learning are positively correlated with diversification rates among primate genera, but not at shallower phylogenetic depths. Given that we find stronger associations when examining older rather than more recent diversification events, we suggest that extinction resistance, as opposed to speciation, may be an important mechanism linking behavioural flexibility and primate diversification. Our results contrast with work linking behavioural flexibility with diversification of birds at various phylogenetic depths. We offer a possible dispersal-mediated explanation for these conflicting patterns, such that the influence behavioural flexibility plays in dictating evolutionary trajectories differs across clades. Our results suggest that behavioural flexibility may act through several different pathways to shape the evolutionary trajectories of lineages.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 0.063 |
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".