Statistical analysis of firefighting and damage caused by fire in mid-rise timber-framed residential buildings compared to other construction types
Bibliographic record
Abstract
The contribution of greenhouse gas emissions in the production of construction materials has sparked a new interest and recent changes in building regulations regarding the use of renewables like wood as the main component in load-bearing elements. Recent regulation changes focus on allowing taller timber structures which bides the question of how to adequately maintain an acceptable level of fire protection considering the characteristically flammable properties of wood-based products. This thesis analysis data on recorded incidents of fires in residential buildings of at least three floors in two countries, Canada and Finland, to try to estimate the impact of building characteristics and firefighting operations in relation to damage caused by fire. Databases used are PRONTO (Finland) and NFID (Canada), the data is analysed using common statistical tools including summary statistics and linear regression. Preluding the statistical analysis is a comparison of building regulations in the two countries, a comparison of general fire statistics and a review of two previous studies on the same subject that utilizes the same databases. The results of the statistical analysis were consistent with what the two studies previously had shown; no clear (positive or negative) correlation between timber-framed buildings and recorded damage seem to be statistically significant, however, timber-framed buildings accounted for more large losses when compared to buildings with non- combustible framing. The linear regression models used predicted simple correlations that were easy to guess intuitively which gives the models some credibility. The results of the comparison were probably skewed by the building height restriction which gave rise to an unfair comparison due to differences in the populations compared.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".