SAFITS - Statistical Analysis of Fires in Timber Structures
Bibliographic record
Abstract
Due to changes of regulations and product development among other things, the number of multi-storey buildings of timber frame or heavy timber construction has increased consistently in the last two decades. The use of a combustible materials in the structure and the relatively short history with such buildings, has led to insurance related questions regarding risks of property loss. Studies of damages in real fire incidents, where a fair comparison between the fire performance of modern multi-storey timber buildings is made, were lacking. In this study damage data of fire incidents from the USA, Canada, Sweden, New Zealand were found and analyzed. Using different methods the extent of fire damage or the financial damage was compared for fires in multistorey buildings of timber construction types and fires in multistorey buildings of other construction types. For each database a qualitative assessment of the reliability and the fairness of the comparison was made. Also, a comparison, for which only a limited number of fire incidents was available, was made between damages caused in sprinklered fires and damages caused in non-sprinklered fires. In addition to the comparative study also qualitative analysis of 33 high damage fire incidents in multistorey timber buildings was made. The goal of this assessment was to identify the most important details to prevent high damage fires.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".