EXTERIOR BASEMENT INSULATION FOR COLD CLIMATES: FURTHER PROOF OF THE NEED TO BUILD BETTER NOW
Bibliographic record
Abstract
Given housing costs, basements are now no longer just utilized as storage spaces, but are often utilized as part of the interior space. Unfortunately, poor moisture management across these walls often leads to mould and mildew growth and poor air quality in basement spaces. As well, basement walls are a substantial component of all heat loss through the building envelope. Considering these problems, and the associated heightened consumer expectations, there are increasing demands on the below-grade portion of the building envelope. This paper compares model thermal and moisture performance and the life cycle economics of exterior basement insulation for four locations across Canada (Halifax, Toronto, Calgary and Vancouver). For each location, three scenarios will be analyzed: one basement built to the prescribed minimum standards established by local building codes, one built to the Model National Energy Code for Houses, and a more sustainable option built to meet the higher thermal and moisture performance needs of tomorrow. Each of these basements will be analyzed and life cycle cost analyses will be carried out using various energy price inflation factors. Considering the relatively long life cycle of homes built today, this paper will show that, from an economic as well as from a performance point of view, there is a compelling need to build better basements now.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".