Human-nonhuman primate interactions: an ethnoprimatological approach
Bibliographic record
Abstract
INTRODUCTION Over the past decades, economic, political and social forces in the developing world have brought about deforestation on a massive scale, depleting the remaining natural habitats of wild nonhuman primates (NHPs). Squeezed into ever smaller domains surrounded by human society, NHPs are coming into increasingly regular contact with humans. Poaching and habitat destruction are recognised dangers to NHP populations in the wild. In contrast, the potentially devastating threat posed by human-to-NHP disease transmission in wild NHP populations is under-appreciated and not well studied (see also Chapter 8). We believe that effective programmes for the conservation of wild NHP populations must acknowledge the interrelation of habitat destruction, bushmeat hunting and human-to-NHP disease transmission. Pathogens endemic to humans have the capacity to devastate NHP populations. This phenomenon has been observed repeatedly in laboratory settings, where epidemics of endemic human diseases such as influenza, tuberculosis, chicken pox and measles can cause mortality rates greater than 90% among NHPs, including animals newly captured from the wild (Padovan & Cantrell, 1986; Mansfield & King, 1998). If endemic human pathogens can cause such profound destruction among captive NHPs, it follows that we should explore the threat that human contact poses to wild NHPs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".