Traditions in wild white-faced capuchin monkeys
Bibliographic record
Abstract
Introduction Primatologists have long recognized that social learning could play an important role in food choice and food processing in primates, since the discovery (by Itani in 1958) of innovative food-processing techniques disseminated among Japanese macaques (see Ch. 10 for a review of subsequent findings). It is somewhat surprising that, after the initial discovery of the importance of social learning in Japanese macaques, practically all subsequent research on social learning in wild nonhuman primates has been on apes (e.g. Boesch, 1996a, 1996b; Boesch and Boesch-Achermann, 2000; Boesch and Tomasello, 1998; McGrew, 1992, 1998; van Schaik, Deaner, and Merrill, 1999; Whiten et al. , 1999; see Chs. 10 and 11). To remedy the gap in what we know about social learning in natural settings in other primates, and because a truly comparative framework is necessary to understand the biological underpinnings of social learning (see Ch. 1), we began a comprehensive study of social learning in wild capuchin monkeys (Cebus spp.). Our study investigates the probable role of social learning in a number of behavioral domains. Capuchins seem particularly likely to exhibit extensive reliance on learning, and social learning in particular, for the following reasons (Fragaszy, Visalberghi, and Fedigan, 2003). Several aspects of capuchin ecology promote behavioral flexibility. First, the genus Cebus occupies a wider geographic area than any other New World genus apart from Alouatta (Emmons, 1997), and it uses many different habitat types. Therefore, capuchins face a wide variety of environmental challenges.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".