The allometry of brain size in Euarchontoglires: clade-specific patterns and their impact on encephalization quotients
Bibliographic record
Abstract
Abstract The timing and nature of evolutionary shifts in the relative brain size of Primates have been extensively studied. Less is known, however, about the scaling of the brain-to-body size in their closest living relatives, i.e., among other members of Euarchontoglires (Dermoptera, Scandentia, Lagomorpha, Rodentia). Ordinary least squares (OLS), reduced major axis (RMA), and phylogenetic generalized least squares (PGLS) regressions were fitted to the largest euarchontogliran data set of brain and body mass, comprising 715 species. Contrary to previous inferences, lagomorph brain sizes (PGLS slope = 0.465; OLS slope = 0.593) scale relative to body mass similarly to rodents (PGLS = 0.526; OLS = 0.638), and differently than primates (PGLS = 0.607; OLS = 0.794). There is a shift in the pattern of the scaling of the brain in Primates, with Strepsirrhini occupying an intermediate stage similar to Scandentia but different from Rodentia and Lagomorpha, while Haplorhini differ from all other groups in the OLS and RMA analyses. The unique brain–body scaling relationship of Primates among Euarchontoglires illustrates the need for clade-specific metrics for relative brain size (i.e., encephalization quotients; EQs) for more restricted taxonomic entities than Mammalia. We created clade-specific regular and phylogenetically adjusted EQ equations at superordinal, ordinal, and subordinal levels. When using fossils as test cases, our results show that generalized mammalian equations underestimate the encephalization of the stem lagomorph Megalagus turgidus in the context of lagomorphs, overestimate the encephalization of the stem primate Microsyops annectens and the early euprimate Necrolemur antiquus, but provide similar EQ values as our new strepsirrhine-specific EQ when applied to the early euprimate Adapis parisiensis.
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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 | 0.001 | 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".