Reliability of Existing Climate Indices in Assessing the Freeze-Thaw Damage Risk of Internally Insulated Masonry Walls
Bibliographic record
Abstract
This paper evaluates the reliability of the currently used climate-based indices in selecting a moisture reference year (MRY) for the freeze-thaw (FT) damage risk assessment of internally insulated solid brick walls. The evaluation methodology compares the ranking of the years determined by the climate-based indices and response-based indices from simulations, regarded as actual performance. The hygrothermal response of an old brick masonry wall assembly, before and after retrofit, was investigated in two Canadian cities under historical and projected future climates. Results indicated that climate-based indices failed to represent the actual performance. However, among the response-based indices, the freeze-thaw damage risk index (FTDR) showed a better correlation with the climate-based indices. Additionally, results indicated a better correlation between the climatic index (CI), the moisture index (MI), and FTDR in Ottawa; however, in Vancouver, a better fit was found between MI and FTDR. Moreover, the risk of freeze-thaw increased considerably after interior insulation was added under both historical and projected future climates. The risk of FT damage would increase for Ottawa but decrease for Vancouver under a warming climate projected in the future, based on the climate scenario used in this study. Further research is needed to develop a more reliable method for the ranking and the selection of MRYs on the basis of climate-based indices that is suitable for freeze-thaw damage risk assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".