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Evaluating the potential of freeze-thaw damage in internally insulated masonry under climate change using different models

2019· article· en· W2971413950 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMATEC Web of Conferences · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicBuilding materials and conservation
Canadian institutionsConcordia UniversityNational Research Council Canada
Fundersnot available
KeywordsEnvironmental scienceFrost (temperature)Climate changeCold climateStormReturn periodMasonryMeteorologyFlood mythEngineeringCivil engineeringGeographyGeology

Abstract

fetched live from OpenAlex

To mitigate the effects of climate change, higher insulation levels in buildings are mandated by the National Energy Code for Buildings. However, increased insulation levels within building envelopes may lead to a greater risk of moisture problems. With a changing climate, higher rainfall intensity, stronger winds and more storms are expected, which may increase wind-driven rain loads on façade and risks for rain penetration damages of building envelopes. This paper aims to present results of the effects of climate change on the freeze-thaw damage risk of internally insulated brick masonry walls of buildings in different Canadian cities, using different freeze-thaw models. Freeze-thaw damage was evaluated using different freeze-thaw models. Simulations were performed using DELPHIN 5.9.4. Results showed potential risk to freeze-thaw in Montreal and Vancouver after retrofit. Under climate change, Winnipeg has the lowest risk to frost damage, though damage functions showed an increase in the level of severity. Comparing the results of different models under a changing climate, the damage functions seemed in a good agreement for most of the cases, except for the Indicative Freeze-Thaw Cycles (IFTC) evaluated in St-Johns. This model counts the number of freeze-thaw cycles based on short duration of freezing and thawing and therefore does not consider longer freeze-thaw period.

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)0.001
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 score0.396
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.095
GPT teacher head0.287
Teacher spread0.192 · 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