Nutritional geometry: gorillas prioritize non-protein energy while consuming surplus protein
Bibliographic record
Abstract
It is widely assumed that terrestrial food webs are built on a nitrogen-limited base and consequently herbivores must compensate through selection of high-protein foods and efficient nitrogen retention. Like many folivorous primates, gorillas' diet selection supports this assumption, as they apparently prefer protein-rich foods. Our study of mountain gorillas (Gorilla beringei) in Uganda revealed that, in some periods, carbohydrate-rich fruits displace a large portion of protein-rich leaves in their diet. We show that non-protein energy (NPE) intake was invariant throughout the year, whereas protein intake was substantially higher when leaves were the major portion of the diet. This pattern of macronutrient intake suggests that gorillas prioritize NPE and, to achieve this when leaves are the major dietary item, they over-eat protein. The concentrations of protein consumed in relation to energy when leaves were the major portion of the diet were close to the maximum recommended for humans and similar to high-protein human weight-loss diets. By contrast, the concentrations of protein in relation to energy when gorillas ate fruit-dominated diets were similar to those recommended for humans. Our results question the generality of nitrogen limitation in terrestrial herbivores and provide a fascinating contrast with human macronutrient intake.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".