Fecal contamination of urban parks by domestic dogs and tragedy of the commons
Bibliographic record
Abstract
Contamination of public parks by dogs is a potential source of conflict among park users, causing "tragedy of the commons" problems. Besides the social conflict, feces can pose serious health risks to both dogs and humans. In this study we analyzed the extent and patterns of the distribution of dog feces in the urban parks of the City of Calgary. We collected dog feces from randomly selected locations in the urban parks. The average density of dog feces by the different dog leash policies of the parks and the distribution pattern of the fecal density within the parks were assessed, and the total contamination of the public parks for the entire city was estimated. We found off-leash parks to be significantly more contaminated than other types of parks. We estimated 127.23 g/ha of dog feces are left unpicked in city parks in total every week. Dog feces were found more often and in greater amount in off-leash parks, and near park entrances and parking lots, than in on-leash parks and away from the park entrances. These results suggest that public park visitors, especially those visiting off-leash parks, are likely to be exposed to large amounts of dog feces. Designation of parks as on-leash and educating dog-owners may be an effective approach for reducing the fecal contamination.
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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 | 0.000 | 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) | 0.001 | 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".