Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Emerging infectious diseases often originate in wildlife, making it important to identify infectious agents in wild populations. It is widely acknowledged that wild animals are incompletely sampled for infectious agents, especially in developing countries, but it is unclear how much more sampling is needed, and where that effort should focus in terms of host species and geographic locations. Here, we identify these gaps in primate parasites, many of which have already emerged as threats to human health. METHODOLOGY: We obtained primate host-parasite records and other variables from existing databases. We then investigated sampling effort within primates relative to their geographic range size, and within countries relative to their primate species richness. We used generalized linear models, controlling for phylogenetic or spatial autocorrelation, to model variation in sampling effort across primates and countries. Finally, we used species richness estimators to extrapolate parasite species richness. RESULTS: We found uneven sampling effort within all primate groups and continents. Sampling effort among primates was influenced by their geographic range size and substrate use, with terrestrial species receiving more sampling. Our parasite species richness estimates suggested that, among the best sampled primates and countries, almost half of primate parasites remain to be sampled; for most primate hosts, the situation is much worse. CONCLUSIONS AND IMPLICATIONS: Sampling effort for primate parasites is uneven and low. The sobering message is that we know little about even the best studied primates, and even less regarding the spatial and temporal distribution of parasitism within species.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".