Modeling variation in the growth of wild and captive juvenile vervet monkeys in relation to diet and resource availability
Bibliographic record
Abstract
OBJECTIVES: To compare longitudinal weight gain in captive and wild juvenile vervet monkeys and conduct an empirical assessment of different mechanistic growth models. METHODS: Weights were collected from two groups of captive monkeys and two consecutive cohorts of wild monkeys until the end of the juvenile period (~800 days). The captive groups were each fed different diets, while the wild groups experienced different ecological conditions. Three different growth curve models were compared. RESULTS: By 800 days, the wild juveniles were lighter, with a slower maximum growth rate, and reached asymptote earlier than their captive counterparts. There were overall differences in weight and growth rate across the two wild cohorts. This corresponded to differences in resource availability. There was considerable overlap in growth rate and predicted adult weight of male and females in the first, but not the second, wild cohort. Maternal parity was not influential. While the von Bertalanffy curve provided the best fit to the data sets modeled together, the Logistic curve best described growth in the wild cohorts when considered separately. CONCLUSIONS: The growth curves of the two captive cohorts are likely to lie near the maximum attainable by juvenile vervets. It may be helpful to include deviations from these rates when assessing the performance of wild vervet monkeys. The comparison of wild and captive juveniles confirmed the value of comparing different growth curve models, and an appreciation that the best models may well differ for different populations. Choice of mechanistic growth model can, therefore, be empirically justified, rather than theoretically predetermined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".