Parrot Trade and the Potential Risk of Psittacosis as a Zoonotic Disease in Indonesian Bird Markets
Bibliographic record
Abstract
Wildlife trade, both legal and illegal, is increasingly recognized as a key factor in the rise of emerging viral infectious diseases, and this is especially apparent in Asia, where large numbers of wildlife are openly offered for sale in bird markets. We here focus on the risk of Psittacosis becoming a zoonotic disease in the wildlife markets of Java and Bali, Indonesia. Psittacosis is particularly prevalent in parrots (hence the name), and the trade in parrots was instrumental in the Great Parrot Fever Pandemic in 1929/1930. Between 2014 and 2023, we conducted 176 surveys of 14 bird markets, during which we recorded 4446 largely wild-caught parrots for sale. On average, each market had nine genera on offer, and the diversity of genera increased with the increasing presence of parrots (up to 16 genera). For most of the bird markets during each survey, parrots from different genera and originating from different parts of the world, were offered for sale alongside each other. Genera offered for sale together did not cluster into natural (geographic) groups. We found no temporal difference in the sale of parrots. We conclude that the omnipresence of wild-caught parrots from various geographic regions in large numbers within the same bird markets increases the risk that psittacosis is present and that this poses a real risk for the zoonotic spread of avian chlamydiosis to humans.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".