Prevalence of Cryptosporidium, Giardia, Salmonella, and Cephalosporin-Resistant E. coli Strains in Canada goose Feces Urban and Peri-Urban Sites in Central Ohio
Bibliographic record
Abstract
Large populations of resident geese can pose a pathogen exposure hazard and disease risk to humans and animals in urban areas.Evidence suggests that waterfowl play a role in pathogen dissemination and disease transmission to humans, however, more definitive data are often needed.This exploratory study sought to identify potential exposure hazards, the first step in risk assessment.This research also discusses doseresponse for protozoan organisms and initiated the exposure assessment process by measuring environmental variables that may be associated with exposure.A total of 199 Canada goose fecal samples were collected from 5 peri-urban and 7 urban sites throughout the Greater Columbus, Ohio area.Samples were collected during two time periods: during 4-11 June, 2013 when geese had just begun their molt, and 16-30 August, 2013 after geese regained flight.Juveniles were distinguished from adults only during the first sample period.Antigen capture enzyme-linked immunosorbent assay (ELISA) was used to detect presence of Giardia and Cryptosporidium.Selective media were used to culture Salmonella and cephalosporin-resistant E. coli strains.Cryptosporidium was the most prevalent pathogen with 44.7% of samples testing positive.Feces collected from urban sites during the first period were 1.86 times more likely to be positive for Cryptosporidium than peri-urban sites (P = 0.10).Forward model selection methods determined that prevalence was positively associated with human population density
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".