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Evaluating Pedestrian Connectivity for Suburban Sustainability

2001· article· en· W2151704436 on OpenAlexafffundabout
Todd A. Randall, Brian W. Baetz

Bibliographic record

VenueJournal of Urban Planning and Development · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsPedestrianSustainabilityTRIPS architectureTransport engineeringRetrofittingDestinationsComputer scienceSustainable transportGeographyEngineering

Abstract

fetched live from OpenAlex

A crucial ingredient for achieving urban sustainability is reducing society's dependence on the automobile. Residents of suburban developments are often dependent on their cars for trips to destinations within the neighborhood because of circuitous street layouts, lack of sidewalks, and long travel distances. The term “pedestrian connectivity” is introduced as a measure of both the directness of route and the route distance for the pedestrian for each home-destination trip. The developed methodology for retrofitting pedestrian enhancements to an existing suburban neighborhood is coded as an ArcView GIS extension. Improvements include the addition of sidewalks and access pathways to isolated cul-de-sacs to make for shorter and more direct routes. Reduced energy consumption, and therefore greater sustainability, may be achieved by having suburban neighborhoods retrofitted in such a way as to allow people to walk for some of their needs and to be well connected to a regional transit system. Modeled results from a neighborhood in Hamilton, Ont., Canada, show how the retrofitted improvements could lead to measurably improved conditions for pedestrians.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.091
GPT teacher head0.398
Teacher spread0.307 · 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

Citations201
Published2001
Admission routes3
Has abstractyes

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