Bibliographic record
Abstract
INTRODUCTION This part explores shared adaptations and challenges acting upon living great apes in the wild that may be linked to their capacities and needs for high-level cognition. Its well-known premise is that their modern adaptations and pressures are valuable proxies for those of their common ancestor. Efforts to assess the cognitive potential of great ape brains have turned up few distinctive features, most predictable from their large body sizes. Assessment remains hampered, however, by very small sample sizes, measurement problems, and extensive individual variability. Cognitive measures typically represent “encephalization,” for instance, in the sense of relative brain size after body size effects have been removed (e.g., EQ (encephalization quotient), neocortical index), and these are problematic as proxies for cognitive potential. These measures also show no greater encephalization in great apes than other anthropoids, which is hard to reconcile with their distinctive cognitive capacities. Features potentially more germane to cognitive capacity have been suggested, such as exceptionally large absolute size, reorganization of information processing functions, or evolution of specific structures, but have received less attention. Large brains are linked with slow life histories–specifically, in primates, with slow maturation concentrated in slow juvenile growth. This points to brain development as a pivotal factor, although how remains unclear. Hypotheses include energetically trading off body growth to support the brain, keeping energy needs low to improve chances of surviving to maturity, and extending time for learning foraging skills.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.431 | 0.297 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".