Assessment of environmental and public health impacts of dog parks in residential neighborhoods: A case study in Toronto, Canada
Bibliographic record
Abstract
The number of pet dogs has been increasing over the last decade, causing more challenges for dog owners taking care of their pets, particularly in current small apartments, as well as larger impacts on public health and environment. Dog owners usually use outdoor public spaces for their dogs to play and defecate. Public spaces are common sites of dog fecal contamination with prevalent rates of gastrointestinal pathogens that are naturally carried by dogs. Dog feces are a serious biohazard. They contain microorganisms that are both pathogenic to humans and resistant to several classes of antibiotics. Extensive spread of these dangerous microorganisms in the area can lead to a pandemic, mainly among children and other vulnerable residents. Biohazard parasites, primarily roundworms of pet dogs, are commonly found in the soil of public parks, particularly in the off-leash areas. These zoonotic parasites infect humans that can result in serious diseases. Direct or indirect contact with polluted soil with pet dogs’ trash is one of the main routes of bacteria transmission from animals to humans. The infection rate among dogs’ shelter workers and dog owners were assessed respectively as 92% and 67%. Scientific research works performed in various countries indicate the existence of canine parasites in more than 50% (even in some cases 67%) of dog parks. This study aims to assess the environmental and health impacts of off-leash dog park in a public park located in a densely-populated neighborhood of Toronto, Canada based on investigations performed in various countries. Biohazard parasites accumulated in a dog park for a longer time act like a hazardous biological agent, are washed off into the underground water table and released off the plants in heat into the air as aerosol, causing serious diseases in 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 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) | 0.002 | 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".