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Mitigation measures for urban heat island and their impact on pedestrian thermal comfort

2021· article· en· W4200289551 on OpenAlexaff
Aytaç Kubilay, Dominik Strebel, Dominique Derome, Jan Carmeliet

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsThermal comfortEnvironmental scienceUrban heat islandPedestrianRelative humidityThermalMeteorologyComputational fluid dynamicsHeat waveVegetation (pathology)MoistureCivil engineeringMarine engineeringEngineeringGeographyGeologyClimate changeAerospace engineering

Abstract

fetched live from OpenAlex

Abstract A multiscale coupled model is presented that allows for the detailed analysis of the local impact of urban heat island mitigation measures. The model uses coupled computational fluid dynamics (CFD) simulations with unsteady heat and moisture transport (HAM) in porous urban materials in order to take into account the dynamic heat and moisture storage in the built environment. A realistic case study is performed for a public urban square in the City of Zurich during heat wave conditions. The impacts of two different mitigation strategies, i.e. adding artificial wetting of pavements and adding vegetation, on pedestrian thermal comfort are evaluated and compared with the existing situation. The results show an improvement in thermal comfort in both conditions. The improvement resulting from the addition of trees is larger and lasts longer due to shadowing effects, even though a reduced ventilation and an increased relative humidity by trees have an adverse effect on the thermal comfort.

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 score0.213
Threshold uncertainty score0.358

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 communication0.0000.001
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 head0.018
GPT teacher head0.239
Teacher spread0.222 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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