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Record W4407616569 · doi:10.1080/14680629.2024.2438340

Utilisation of steel slag aggregates to propose novel asphalt pavement structures alleviating urban heat islands

2025· article· en· W4407616569 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueRoad Materials and Pavement Design · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAsphalt pavementAsphaltSlag (welding)Urban heat islandMaterials scienceAsphalt concreteEnvironmental scienceForensic engineeringComposite materialMetallurgyGeotechnical engineeringEngineeringGeography

Abstract

fetched live from OpenAlex

Asphalt pavements release their absorbed heat, exacerbating an urban issue called urban heat island (UHI). In this study, the base course and the interface of the asphalt mixture and the base course were modified with steel slag aggregates (33, 66, and 100%) and a conductive prime coat (40% steel slag powder) to mitigate this issue. The heat transfer rates of developed specimens were then investigated using a solar simulation setup. The thermal properties of these specimens were also measured using a C-Therm device, developing a numerical model using ANSYS Fluent software and computational fluid dynamics (CFD) algorithms. The base courses containing 66% steel slag aggregates and the conductive prime coat showed the best performance. These structures decreased the pavement surface temperature by 13% and nighttime outgoing heat by 38% and 33%, respectively. The air temperature near the surface of these pavement structures was also reduced by 14%.

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)0.000
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 score0.052
Threshold uncertainty score0.581

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 communication0.0000.000
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 head0.019
GPT teacher head0.239
Teacher spread0.220 · 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