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Wind-driven rain and future risk to built heritage in the United Kingdom: Novel metrics for characterising rain spells

2018· article· en· W2806204859 on OpenAlexfundno aff
Maureen Young, Dawson Stelfox, Joanne M. Curran, Heather Viles

Bibliographic record

VenueThe Science of The Total Environment · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilNatural Sciences and Engineering Research Council of CanadaDepartment for Environment, Food and Rural Affairs, UK Government
KeywordsEnvironmental scienceMeteorologyClimate changeWind speedClimate zonesClimatologyGeographyGeology

Abstract

Wind-driven rain (WDR) is rain given a horizontal velocity component by wind and falling obliquely. It is a prominent environmental risk to built heritage, as it contributes to the damage of porous building materials and building element failure. While predicted climate trends are well-established, how they will specifically manifest in future WDR is uncertain. This paper combines UKCP09 Weather Generator predictions with a probabilistic process to create hourly time series of climate parameters under a high-emissions scenario for 2070-2099 at eight UK sites. Exposure to WDR at these sites for baseline and future periods is calculated from semi-empirical models based on long-term hourly meteorological data using ISO 15927-3:2009. Towards the end of the twenty-first century, it is predicted that rain spells will have higher volumes, i.e. a higher quantity of water will impact façades, across all 8 sites. Although the average number of spells is predicted to remain constant, they will be shorter with longer of periods of time between them and more intense with wind-driven rain occurring for a greater proportion of hours within them. It is likely that in this scenario building element failure - such as moisture ingress through cracks and gutter over-spill - will occur more frequently. There will be higher rates of moisture cycling and enhanced deep-seated wetting. These predicted changes require new metrics for wind-driven rain to be developed, so that future impacts can be managed effectively and efficiently.

Stored with the screening record, where it is evidence for the labels above.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

All three models called this out of scope.

stratum: fund_new · design weight: 1678.90 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: empirical
about Canada: no
confidence: high

Climate modeling of wind driven rain risk to built heritage; a domain science question.

GPT-5.6 (high)OUT
genre: empirical
about Canada: no
confidence: high

The study models wind-driven rain and risks to built heritage.

Grok 4.5OUT
genre: empirical
about Canada: no
confidence: high

Climate/heritage science on wind-driven rain risk to UK buildings; object is built heritage exposure, not research.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.023
GPT teacher head0.243
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations66
Published2018
Admission routes1
Has abstractyes

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