Pathogen Loading From Canada Geese Faeces in Freshwater: Potential Risks to Human Health Through Recreational Water Exposure
Bibliographic record
Abstract
Canada geese (Branta canadensis) faeces have been shown to contain pathogenic protozoa and bacteria in numerous studies over the past 15 years. Further, increases in both the Canada geese populations and their ideal habitat requirements in the United States (US) translate to a greater presence of these human pathogens in public areas, such as recreational freshwater beaches. Combining these factors, the potential health risk posed by Canada geese faeces at freshwater beaches presents an emerging public health issue that warrants further study. Here, literature concerning human pathogens in Canada geese faeces is reviewed and the potential impacts these pathogens may have on human health are discussed. Pathogens of potential concern include Campylobacter jejuni, Salmonella Typhimurium, Listeria monocytogenes, Helicobacter canadensis, Arcobacter spp., Enterohemorragic Escherichia coli pathogenic strains, Chlamydia psitacci, Cryptosporidium parvum and Giardia lamblia. Scenarios presenting potential exposure to pathogens eluted from faeces include bathers swimming in lakes, children playing with wet and dry sand impacted by geese droppings and other common recreational activities associated with public beaches. Recent recreational water-associated disease outbreaks in the US support the plausibility for some of these pathogens, including Cryptosporidium spp. and C. jejuni, to cause human illness in this setting. In view of these findings and the uncertainties associated with the real health risk posed by Canada geese faecal pathogens to users of freshwater lakes, it is recommended that beach managers use microbial source tracking and conduct a quantitative microbial risk assessment to analyse the local impact of Canada geese on microbial water quality during their decision-making process in beach and watershed management.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".