Group versus population level demographics: An analysis of comparability using long term data on wild white‐faced capuchin monkeys (<i>Cebus capucinus imitator</i>)
Bibliographic record
Abstract
Primates have long been used as indicator species for assessing overall ecosystem health. However, area-wide census methods are time consuming, costly, and not always feasible under many field conditions. Therefore, it is important to establish whether monitoring a subset of a population accurately reflects demographic changes occurring in the population at large. Over the past 35 years, we have conducted 15 area-wide censuses in Sector Santa Rosa, Costa Rica. These efforts have revealed important trends in population growth patterns of capuchin monkeys following the protection and subsequent regeneration of native forests. During this same period, we have also intensively studied a subset of the capuchin groups. Comparing these two datasets, we investigate whether the population structures of the closely monitored groups are reliable indicators of area-wide demographic patterns. We compare the overall group size and the individual age/sex class compositions of study groups and nonstudy groups (i.e., those contacted during area-wide censuses only). Our study groups contained more individuals overall with a larger proportion of infants, and there were indications that the proportion of adult and subadult males was lower. These differences can be ascribed either to sampling errors or real differences attributable to human presence and/or better habitat quality for the study groups. No other sex/age classes differed, and major demographic changes were simultaneously evident in both study and nonstudy groups. This study suggests that the Santa Rosa capuchin population is similarly impacted by large-scale ecological patterns observable within our study groups.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".