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Record W2139701365 · doi:10.1109/eicccc.2006.277247

Weathering of Building Infrastructure and the Changing Climate: Adaptation Options

2006· article· en· W2139701365 on OpenAlexaff
Heather Auld, Joan Klaassen, Neil Comer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsImpactEnvironment and Climate Change Canada
Fundersnot available
KeywordsWeatheringEnvironmental scienceDurabilityClimate changeFrost weatheringEarth scienceGeologySoil scienceMaterials science

Abstract

fetched live from OpenAlex

The changing climate will impact infrastructure through gradual changes in weather patterns, increasing variability and potential increases in extremes. Although most concerns have focused on changing extremes, the changes in day-to-day weathering processes may be equally important. These significant day-to-day weathering processes include wind-driven rain, freeze-thaw cycles, frost penetration, wetting and drying, wind-driven abrasive materials, the action of broad spectrum solar radiation and ultraviolet (UV) radiation, and atmospheric chemical deposition on materials. Adaptation options need to be developed to consider changing weathering processes, including more frequent freeze-thaw cycles in colder regions, potentially increased atmospheric chemical deposition, initially increasing UV levels and changes to precipitation regimes. Since buildings are particularly vulnerable to weathering impacts that compromise their durability and resilience to extremes over time, it will become increasingly important in future to ensure that building envelopes and enclosures are able to resist wind actions and to prevent moisture from entering the structure. Many of the required adaptation actions may take the form of different formulations for materials or different engineering practices to ensure greater durability or requirements in standards for preventative maintenance.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.122

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 head0.005
GPT teacher head0.193
Teacher spread0.188 · 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

Citations16
Published2006
Admission routes1
Has abstractyes

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