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Record W4412469063 · doi:10.1080/23789689.2025.2532301

Evaluating the effectiveness of high thermal resistance aggregates in asphalt mixtures for Urban Heat Island (UHI) mitigation

2025· article· en· W4412469063 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSustainable and Resilient Infrastructure · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsUrban heat islandAsphaltEnvironmental scienceResistance (ecology)Materials scienceMeteorologyGeographyComposite material

Abstract

fetched live from OpenAlex

This study investigates how different asphalt mixtures can mitigate the Urban Heat Island (UHI) effect by analysing their thermal properties, including conductivity, heat storage, and thermal inertia. Limestone asphalt mixtures (LM), characterised by high thermal inertia, were compared with glass (GM), ceramic (CM), and clay brick (CBM) mixtures, which have lower thermal inertia. Laboratory and field tests conducted during the summers of 2022 and 2023 evaluated how these materials respond to solar radiation and ambient conditions. LM exhibited more stable surface temperatures due to slower heating and cooling, whereas GM, CM, and CBM cooled more quickly at night, which helped reduce heat accumulation and UHI intensity. The research highlights the novel use of recycled materials—glass, ceramic, and brick—in asphalt mixtures and emphasises the importance of optimising air voids to enhance thermal performance. These findings support the development of sustainable asphalt designs that promote daytime heat dissipation and nighttime cooling in urban areas.

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.

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 score0.130
Threshold uncertainty score0.395

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 head0.004
GPT teacher head0.249
Teacher spread0.246 · 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