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Record W3160272274 · doi:10.14293/icmb210034

Wind-driven rain exposure and moisture damage risk of heritage facades

2021· article· en· W3160272274 on OpenAlexaffabout
Aytaç Kubilay, John Bourcet, Jessica Gravel, Xiaohai Zhou, Dominique Derome, Jan Carmeliet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicBuilding materials and conservation
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsFacadeEnvironmental scienceMoistureMeteorologyComputational fluid dynamicsWind speedAtmospheric sciencesCivil engineeringGeographyEngineeringGeology

Abstract

fetched live from OpenAlex

<p class="first" id="d994860e90">Accurate estimation of wind-driven rain (WDR) load on building facades is of paramount importance for the assessment of moisture-induced damage risks. Computational fluid dynamics (CFD) simulations of WDR are performed for the Parliament Buildings in Ottawa, Canada. Combined with a statistical analysis of the long-term local meteorological conditions, zones of facades receiving the highest amount of WDR are determined. Then, for critical locations on the facade, the Climatic Index is used as a metric, taking into account the total wetting load and total drying potential, in order to identify the façade locations and periods that show high risk of moisture damage.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.999

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)0.0010.000

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 head0.011
GPT teacher head0.194
Teacher spread0.183 · 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.

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

Citations0
Published2021
Admission routes2
Has abstractyes

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