Estrogenic plant foods of red colobus monkeys and mountain gorillas in uganda
Bibliographic record
Abstract
Phytoestrogens, or naturally occurring estrogen-mimicking compounds, are found in many human plant foods, such as soybeans (Glycine max) and other legumes. Because the consumption of phytoestrogens may result in both health benefits of protecting against estrogen-dependent cancers and reproductive costs of disrupting the developing endocrine system, considerable biomedical research has been focused on the physiological and behavioral effects of these compounds. Despite this interest, little is known about the occurrence of phytoestrogens in the diets of wild primates, nor their likely evolutionary importance. We investigated the prevalence of estrogenic plant foods in the diets of two folivorous primate species, the red colobus monkey (Procolobus rufomitratus) of Kibale National Park and mountain gorilla (Gorilla beringei) of Bwindi Impenetrable National Park, both in Uganda. To examine plant foods for estrogenic activity, we screened 44 plant items (species and part) comprising 78.4% of the diet of red colobus monkeys and 53 plant items comprising 85.2% of the diet of mountain gorillas using transient transfection assays. At least 10.6% of the red colobus diet and 8.8% of the gorilla diet had estrogenic activity. This was mainly the result of the red colobus eating three estrogenic staple foods and the gorillas eating one estrogenic staple food. All estrogenic plants exhibited estrogen receptor (ER) subtype selectivity, as their phytoestrogens activated ERβ, but not ERα. These results demonstrate that estrogenic plant foods are routinely consumed by two folivorous primate species. Phytoestrogens in the wild plant foods of these two species and many other wild primates may have important implications for understanding primate reproductive ecology.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".