Comparing Canadian and Norwegian moisture indices for building climate adaptation
Bibliographic record
Abstract
Abstract To evaluate the suitability of materials and solutions in building envelopes, it is necessary to quantify the relevant climate loads. The critical climate load is typically a combination of multiple parameters, such as temperature and precipitation. Climate indices may be used for finding critical climate loads, and their use helps guide design choices when adapting to local climates. The purpose of this study is to evaluate the suitability of the Canadian Moisture Index (MI) for use in Norway. The values of MI are linked to design recommendations in the Canadian building code, thus enabling a tangible link between index values and moisture design practice. MI has been calculated for 12 locations in Norway, and compared to two indices already in national use: the driving rain index (DRI) and wood decay potential index (WDPI). The applicability of a climate index as a design tool depends on (1) describing a relevant climate stress; (2) logical differentiation of values, and; (3) translating index values to design recommendations. These are fulfilled for MI in a Canadian context, thus making it applicable as a design tool. However, significant adaptation may be required for the index to be adopted to a Norwegian context. As MI and DRI have a similar field of application, introducing MI into a Norwegian context may therefore be redundant. A drawback with the Norwegian indices is the relative weak link between index values and design recommendations, thus further development of recommendations based on index values may improve their applicability as design tools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".