A Benefit-Cost Analysis of Impact-Resistant Asphalt Shingle Roofing--Overview
Bibliographic record
Abstract
The Institute for Catastrophic Loss Reduction creates and disseminates disaster resilience knowledge for Canada. Among the catastrophes ICLR addresses are hailstorms, one of Canada’s most serious natural hazards. Hail costs $400 million annually. A June 2020 hailstorm at the edge of Calgary damaged 77,000 homes and cost $1.4 billion. Much of that money paid for roof repairs. A direct hit on Calgary could be 5 to 10 times worse. Though the hailstorm is inevitable, the catastrophe is not. This document summarizes a study of one way that homeowners and insurers can prevent costly hail damage: by using impact-resistant asphalt shingle roofs instead of standard shingles. Impact-resistant roof shingles look like ordinary shingles, but have material that makes them resistant to hail damage. When struck by large hailstones, they resist pits and fractures that would otherwise allow water to pool or penetrate beneath them. And they resist cosmetic damage like the loss of granules: the specks that cover the shingle surface.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".