Linking feeding ecology and population abundance: a review of food resource limitation on primates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We review studies that consider how food affects primate population abundance. In order to explain spatial variation in primate abundance, various correlates that parameterize quality and quantity of food in the habitat have been examined. We propose two hypotheses concerning how resource availability and its seasonality determine animal abundance. When the quality of fallback foods (foods eaten during the scarcity of preferred foods) is too low to satisfy nutritional requirement, total annual food quantity should determine population size, but this relationship can be modified by the quality or the quantity of fallback foods. This mechanism has been established for Japanese macaques and sportive lemurs that survive lean seasons by fat storage or extremely low metabolism. Second, when fallback food quality is high enough to satisfy nutritional requirement but quantity is limited, quantity of fallback food should be a limiting factor of animal abundance. This is supported by the correlation between fig density, which is a high‐quality fallback food, and gibbon and orangutan abundance. For a direct test of these hypotheses, we need more research that determines both the quality of food that animals require to satisfy their nutritional requirement and the quantity of food production. Leaves are often regarded as superabundant, but this assumption needs careful examination.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it