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Record W4322725044 · doi:10.1038/s41598-023-30225-7

Fecal contamination of urban parks by domestic dogs and tragedy of the commons

2023· article· en· W4322725044 on OpenAlexafffundabout
Kensuke Mori, Melanie Rock, Gavin R. McCormack, Stefano Liccioli, Dimitri Giunchi, Danielle J. Marceau, Emmanuel Stefanakis, Alessandro Massolo

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsParks CanadaUniversity of Calgary
FundersAlberta InnovatesUniversity of Calgary
KeywordsFecesPublic parkGeographyTragedy of the commonsDistribution (mathematics)Veterinary medicineEnvironmental healthSocioeconomicsBiologyEcologyCommonsEnvironmental planningMedicineSociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.158

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.312
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2023
Admission routes3
Has abstractyes

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