Reproductive seasonality in wild Sanje mangabeys (Cercocebus sanjei), Tanzania: Relationship between the capital breeding strategy and infant survival
Bibliographic record
Abstract
The reproductive seasonality model states that it is adaptive for species in seasonally variable environments to temporally cluster reproductive events around periods of resource availability. Many studies have examined links between seasonal reproduction and phenological events, though few studies have fully tested the adaptive hypothesis by examining the effects of reproductive timing on outcomes. Our study tests the predictions of the model in an African cercopithecine, the Sanje mangabey, by examining the impact of food availability on the timing of conception, birth and weaning, and the relationship of reproductive timing and female energy balance (urinary C-peptide) to infant survival. From September 2008 through 2010, 28 infants were born. Distribution of conceptions was non-uniform, with a peak between January and March. There was a significant positive correlation between mean monthly fruit availability and number of conceptions per month. An increased food supply supports a positive energy balance, maximizing the potential for conception; a pattern found among many cercopithecines. Mothers that conceived within the peak period also exhibited higher levels of urinary C-peptide during preconception and early gestation, compared to conceptions outside the peak period. This strategy increased the probability of survival to year one, as it was significantly higher for infants conceived during the peak conceptive season. These results support the reproductive seasonality model and demonstrate that the timing of conception is critical for mangabey reproductive success.
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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 | 0.001 |
| 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 | 0.000 |
| 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".