MétaCan
Menu
Back to cohort
Record W4389100380 · doi:10.17352/2455-488x.000073

Cool walkability planning: Providing pedestrian thermal comfort in hot climate cities

2023· article· en· W4389100380 on OpenAlexaff
Todd Litman

Bibliographic record

VenueJournal of Civil Engineering and Environmental Sciences · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsTransport Canada
Fundersnot available
KeywordsWalkabilityUrbanizationEnvironmental sciencePedestrianUrban heat islandThermal comfortTRIPS architectureUrban climateClimate changeClimate zonesGeographyMeteorologyTransport engineeringBuilt environmentPhysical geographyCivil engineeringEngineeringEconomic growthEconomics

Abstract

fetched live from OpenAlex

Global warming and urbanization are increasing the number of people living in cities that experience extreme heat. This makes walking uncomfortable, unattractive, and unhealthy and causes travelers to drive for trips that could be made on foot. To address these problems hot-climate cities can create networks of shadeways (shaded sidewalks) and pedways (enclosed, climate-controlled walkways). This article introduces the Cool Walkshed Index (CWI) which rates pedestrian thermal protection from A (best) to F (worst). Currently, most urban neighborhoods have CWI E (incomplete sidewalk networks) or D (complete sidewalk networks). Moderate-heat cities should aspire to CWI C (shaded sidewalks on busy routes); high-heat cities, with temperatures that frequently exceed 38 °C (100 °F) should aspire to CWI B (most buildings located within 300 m of enclosed, climate-controlled pedways); and extreme-heat cities, with temperatures that frequently exceed 43 °C (110° F) should aspire to CWI A (most buildings are located less than 100 m of enclosed, climate-controlled pedways). Analysis in this study indicates that the additional costs of these facilities can be repaid many times over through road, parking, and vehicle savings, and increased local property values.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.213
Teacher spread0.198 · 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

Citations16
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Civil Engineering and Environmental SciencesSame topicUrban Heat Island MitigationFrench-language works237,207