Guidelines and considerations for capturing and collaring wild primates: a case study with capuchin monkeys (Sapajus nigritus cucullatus)
Bibliographic record
Abstract
Many aspects of the ecology, evolution and social behavior of wild-living primates remain un-explored and require further investigation. While long-term field studies are crucial for addressing conservation concerns for many primates' species, acquiring the necessary data is often challenging, often due to difficulties in locating study groups. Radio-telemetry has significantly facilitated the study of primates and other animals living in tropical forests. However, there are important practical challenges in the process of capturing and releasing animals after placement of telemetry collars. In this study, we report guidelines and considerations for capturing and collaring wild capuchin monkeys, Sapajus nigritus cucullatus, in the Atlantic Forest of Argentina. Our ultimate goal is to contribute to making captures safer, preventing harm and stress to animals when using radio-telemetry in monitoring strategies for conservation of this primate species. These methods can be useful for researchers using field capture and radio-telemetry for monitoring groups or populations of wild primates, specifically wild Sapajus.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".