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Dog-walking in dense compact areas: The role of neighbourhood built environment

2019· article· en· W2990103638 on OpenAlexaff
Mohammad Javad Koohsari, Tomoki Nakaya, Gavin R. McCormack, Ai Shibata, Kaori Ishii, Akitomo Yasunaga, Yung Liao, Koichiro Oka

Bibliographic record

VenueHealth & Place · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Calgary
FundersMinistry of Education, Culture, Sports, Science and Technology
KeywordsNeighbourhood (mathematics)Built environmentPhysical activityGeographyEnvironmental healthBusinessSocioeconomicsMedicineEcologySociologyPhysical medicine and rehabilitationMathematicsBiology

Abstract

fetched live from OpenAlex

There is a dearth of evidence about how high-density living may influence dog-walking behaviour. We examined associations between neighbourhood built environment attributes and dog-ownership and dog-walking behaviour in Japan. Data from 1058 participants were used. The dog-ownership was 18.8%. All neighbourhood built attributes (excluding availability of parks) were negatively associated with dog-ownership. Among dog-owners, these same attributes were positively associated with any dog-walking in a usual week and with achieving 150-min per week of physical activity through dog-walking alone. These findings provided evidence on the importance of neighbourhood built environment attributes on dog-ownership and dog-walking behaviour in dense and compact areas. The urban design and public health implication of these findings is that the built environment attributes in high-density living areas may have different impacts on dog-ownership and dog-walking: while living in a walkable neighbourhood may not be conducive to dog-ownership, it may support dog-walking in such areas. Programs targeting dog-owners in high-density areas might be needed to encourage them to walk their dogs more. If successful, these programs could contribute to higher physical activity levels among dog-owners.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.307

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 head0.322
Teacher spread0.309 · 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 designObservational
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

Citations28
Published2019
Admission routes1
Has abstractyes

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