The nutritional value of feeding on crops: Diets of vervet monkeys in a humanized landscape
Bibliographic record
Abstract
Abstract Anthropogenic influences have dramatically altered the environments with which primates interact. In particular, the introduction of anthropogenic food sources to primate groups has implications for feeding behaviour, social behaviour, activity budgets, demography and life history. While the incorporation of anthropogenic foods can be beneficial to primates in a variety of nutritional ways including increased energetic return, they also carry risks associated with proximity to humans, such as risk of being hunted, disease risk and risk of conflict. Given such risks, we initiated a 3‐year study where we sought to understand the underlying nutritional motivations for anthropogenic food resource use by vervet monkeys ( Cercopithecus aethiops ) in the humanized matrix surrounding the Nabugabo Field Station in central Uganda. Feeding effort, defined as proportion of feeding scans spent on anthropogenic food, was not associated with ripe fruit availability nor with crop availability as determined by phenological monitoring. Likewise, there was no difference in the protein, fibre, or lipid composition of crop food items compared to wild food items. Individuals spent less time feeding overall in months over the 3 years with a higher proportion of time spent feeding on crop foods, suggesting a potential benefit in terms of accessibility (reduction in the proportion of activity budget devoted to feeding).
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".