MULTISPECIES SHIGELLA FLEXNERI OUTBREAK IN A ZOOLOGICAL COLLECTION COINCIDING WITH A CLUSTER IN THE LOCAL HUMAN POPULATION
Bibliographic record
Abstract
Shigella flexneri is a nonmotile gram-negative bacillus that affects humans and nonhuman primates. In August 2021, 15 primates at the ABQ BioPark demonstrated clinical signs of Shigella infection: 3 out of 4 Sumatran and hybrid orangutans (Pongo abelii), 6 out of 8 gorillas (Gorilla gorilla), 2 out of 9 chimpanzees (Pan troglodytes), and 4 out of 4 siamangs (Hylobates syndactylus). Three siamangs and one gorilla succumbed to complications of shigellosis during the initial outbreak and a chimpanzee died 10 mon later. Although it is well documented that Shigella may cause morbidity and mortality in nonhuman primates, the rapid and devastating nature of the outbreak, the difference from previous reports in zoological collections (enzootic vs outbreak), and the chronological overlap with the increase in human cases in the region makes discussion of this Shigella outbreak of significance. The cases presented here are significantly different than previous reports, because these were part of an outbreak that arose and subsided, versus other reports where the authors describe an enzootic disease with persistently infected animals. Close communication with the New Mexico Department of Health allowed for the investigation into possible sources of the outbreak, recommendations regarding biosecurity protocols, and staff education.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".