Dietary patterns in Perrier's sifakas (<i>Propithecus diadema perrieri</i>): A preliminary study
Bibliographic record
Abstract
Some lemur species range into only one habitat type, whereas others range into a variety of habitats. Because plant community structure can differ between habitats, dietary patterns may vary for conspecific groups of primates that range into more than one type of habitat. The goal of our study was to determine how habitat variation influences dietary patterns in Perrier's sifakas (Propithecus diadema perrieri) that range into both dry and riparian forests in northern Madagascar. We collected 542 hr of data on the behavior and diet of two groups of P.d. perrieri from 7 June to 4 August 1998 at Camp Antobiratsy in Analamera Special Reserve, Madagascar. We computed indices of dietary diversity for each group and dietary/plant species similarity between groups. P.d. perrieri in group 1 fed predominantly in dry forest (72.7% of feeding records, n=660), whereas those in group 2 fed most often in riparian forest (73.7% of feeding records, n=666). The index of dietary similarity (0.986) was significantly higher than the index of plant species similarity (0.767). Although the P.d. perrieri in the two study groups fed predominantly in different forest habitats, they ate similar food items in very comparable proportions (but not from the same plant species). However, based on habitat availability measures, neither group fed where they were expected to feed.
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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 | 0.001 | 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".