Cross‐site differences in foraging behavior of white‐faced capuchins (<i>Cebus capucinus</i>)
Bibliographic record
Abstract
Researchers have identified a variety of cross-site differences in the foraging behavior of free-ranging great apes, most notably among chimpanzees (Pan troglodytes) and more recently orangutans (Pongo pygmaeus), that are not due to obvious genetic or ecological differences. These differences are often referred to as "traditions." What is not known is whether this high level of interpopulation variation in behavior is limited to hominoids. In this study, we use long-term data from three Costa Rican field sites that are geographically close and similar ecologically to identify potential foraging traditions in white-faced capuchins (Cebus capucinus). Foraging traditions are predicted in Cebus because of many behavioral and morphological convergences between this genus and the great apes. The processing techniques used for the same food species were compared across sites, and all differences found were classified as present, habitual, or customary. Proximity data were also analyzed to determine if social learning mechanisms could explain variation in foraging behavior. Of the 61 foods compared, we found that 20 of them are processed differently by capuchins across sites. The differences involve pound, rub, tap, "fulcrum," "leaf-wrap," and "army ant following." For most of the differences with enough data to analyze, the average proximity score of the "matched" dyads (two individuals within a group who shared a "different" processing technique) was statistically higher than the average proximity score of the remaining "unmatched" dyads.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".