Prediction and Ranking of Moisture Severity of Climate Years Using Supervised Projection to Latent Structures Applied to Tall Wood Buildings
Bibliographic record
Abstract
Hygrothermal simulation tools are commonly used to assess the moisture performance of building envelope components. Owing to the computational costs required to complete simulations over the long-term, one approach to reduce simulation time when undertaking hygrothermal design analysis is to select representative year(s) amongst sets of long-term climate data. To properly select these moisture reference year(s), a method is required to rank or predict the moisture severity of climate years for sets of long-term climate data. Several methods are used in the literature for this purpose, but none seems to be sufficiently accurate. In this study, the supervised projection to latent structures, also known as partial least squares regression, was trained and validated on data obtained from hygrothermal simulations of tall wood building wall assemblies for several cities across Canada. Models developed at the city level, for a given greenhouse gas emission scenario or time period, or encompassing historical and future time periods, showed comparable scores for ranking. In respect to prediction of the moisture severity of climate year sets, models developed at the city level were shown to be more accurate.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".