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Record W2318758427 · doi:10.1080/17549175.2014.990916

Improving walkability for seniors through accessibility to food stores: a study of three areas of Greater Montreal

2014· article· en· W2318758427 on OpenAlexafffundabout
Paula Negron-Poblete, Anne‐Marie Séguin, Philippe Apparicio

Bibliographic record

VenueJournal of Urbanism International Research on Placemaking and Urban Sustainability · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité de Montréal
FundersUniversité du MaineInstitut national de la recherche scientifiqueUniversité Laval
KeywordsWalkabilityPedestrianTransport engineeringPublic transportScale (ratio)GeographyBusinessBuilt environmentEngineeringCivil engineeringCartography

Abstract

fetched live from OpenAlex

The aging of suburbs requires that pedestrian accessibility be favored in this type of environment, because walking is a key element in the quality of life of seniors. This article analyzes the potential for accessibility by foot in three inner suburbs of the Greater Montreal Area. Accessibility was calculated using walking distances throughout the street network. This analysis was complemented by an observation of physical-spatial characteristics likely to affect walking among seniors. Pedestrian accessibility is influenced not only by long travel distances but also by various obstacles that result from land-use decisions in favor of motorized travel on a regional scale. This article reveals the necessity for urban planners to find a balance between local accessibility by foot and public transit, and regional accessibility by motorized transport.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.070
GPT teacher head0.408
Teacher spread0.338 · 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 source (direct Gemma or distilled Codex), 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

Citations31
Published2014
Admission routes3
Has abstractyes

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