Food for Thought: Lactating Coquerel’s Sifaka (Propithecus Coquereli) Eat Foods High in Protein and Fiber During the Lean Season
Bibliographic record
Abstract
Abstract Infant-bearing, Coquerel’s sifaka (Propithecus coquereli) undergo gestation during a lean seasonal climate with weaning occurring during the abundant season. During this time, nutrient demand increases due to placental transport to the fetus and to the infant postpartum by milk. Females respond to this increased demand by ingesting larger food quantities, reducing expenditure, and/or using their nutrient stores. We collected foods (N=75) exploited by lactating females (N=10) in Ankarafantsika National Park, Madagascar to examine the nutritional landscape within which sifakas forage. We measured food nitrogen, neutral detergent fiber (NDF), acid detergent fiber (ADF), gross energy (GE) and ash to estimate crude protein (CP), available protein (AP), fiber, mineral content and metabolizable energy (ME). Two significant PCA (principal component analysis) axes corresponded to high protein and high fiber-low ME explaining 91.6% of the variance. Cluster 1 is categorized by foods that contained higher AP and cluster 2 is categorized by higher fiber foods. P. coquereli rely on a diverse range of foods inclusive of those with high AP and ME, but also high fiber foods with low ME. We hypothesize that the high fiber, low ME foods may be important for maintaining the gut microbiome.
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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 | 0.001 | 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) | 0.004 | 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".