What is the Predictive Power of the Colobine Protein-to-Fiber Model and its Conservation Value?
Bibliographic record
Abstract
Predicting variation in animal abundance across time and space has proven very difficult; however, a model exists to predict the biomass of small folivorous primates that has considerable correlative support. This model suggests that the protein-to-fiber ratio of leaves in a habitat can predict folivore biomass. Here we present an experimental test of this protein-to-fiber model to assess if the number of infant monkeys per female and group size can be predicted based on the leaf chemistry of a habitat. We expected regenerating forest in Kibale National Park, Uganda to have leaves with higher concentrations of crude protein and lower concentrations of fiber than old-growth forest trees, and consequently, we expected a greater number of infants per female in the folivorous red colobus ( Procolobus rufomitratus) with access to this area. As predicted, regenerating forests did have trees with leaves with high concentrations of protein and low concentrations of fiber, but there was no corresponding change in the demographic structure of red colobus groups. We also tested whether energy was a potential determinant of these parameters, but found no evidence for its importance. Our findings support recent studies that are critical of the protein-to-fiber model, which lead us to question the model's generality, particularly for conservation and management.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".