Investigation of Fire Incidents and Associated Damage to Buildings
Bibliographic record
Abstract
Among different types of man-made and nature-made disasters, fire constitutes a significant threat to both people and structures. This study provides fire incident data including the origin and cause, as well as the extent and severity of real fire incidents that occurred in around 60 wood–frame residential buildings in the province of Ontario, Canada. Analysis of the data showed that most of the fires in the residential building were electrical fires; however, the causes for many of the fires were reported as “undetermined.” The primary places of origin of the fires were found to be the garages and kitchens. Sunday was the day of the week with the highest number of fire incidents. It was observed that floor joists and roof framings were the most vulnerable structural elements, and poor fire performance of I-joists often resulted in total loss. To protect floor framings from fire, it is recommended that the exceptions to the provisions of section R302.13 of the 2015/2018 IRC be eliminated so that all types and sizes of floor framings be covered with gypsum boards. Similar provisions shall be added to Part 9 of NBC as well as OBC and other Canadian provincial building codes. It is also recommended that I-joist floor systems and roof framings be sealed with a fire-protective coating after installation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".