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Record W2913782355 · doi:10.1139/cjce-2018-0594

Does historic construction suffer or benefit from the urban heat island effect in Ghent and global warming across Europe?

2019· article· en· W2913782355 on OpenAlexvenueno aff
Isabeau Vandemeulebroucke, Klaas Calle, Steven Caluwaerts, Tim De Kock, Nathan Van Den Bossche

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsnot available
FundersVlaamse regeringFonds Wetenschappelijk Onderzoek
KeywordsRetrofittingClimate changeEnvironmental scienceUrban heat islandFrost (temperature)Global warmingMasonryLatitudeClimatologyPhysical geographyMeteorologyGeographyGeologyCivil engineeringEngineering

Abstract

fetched live from OpenAlex

Renovating historical buildings with valuable facades often includes interior retrofitting, perhaps entailing an increased durability risk. However, the urban heat island effect and the ongoing climate change might mitigate the severity of frost action and mould growth. By means of heat air moisture (HAM) simulations in Delphin, this study evaluates interior retrofitting of solid masonry on three scales. First, the sensitivity to the intra-urban climatic differences of the freeze–thaw cycles in Ghent is analysed. Secondly, the spatial pattern of freeze–thaw behaviour across Europe is assessed. Finally, the influence of observed climate change on the European freeze–thaw pattern is investigated. A decreasing number of critical freeze–thaw cycles is found when comparing the rural area with the city centre of Ghent. Furthermore, due to climate change, the number of freeze–thaw cycles across Europe generally decreases as well, except at northern latitudes exposed to increased wind-driven rain loads.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.996

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 head0.003
GPT teacher head0.168
Teacher spread0.165 · 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

Citations28
Published2019
Admission routes1
Has abstractyes

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