Fatal fire scenarios in Canadian houses
Bibliographic record
Abstract
In Ontario, from 1995 to 1998, 65% of fires and 95% of deaths occurred in residential buildings. Within these residential fires, approximately 66% of the fires and 57% of the deaths occurred in houses (detached, semi-detached and attached houses). From 1995 to 1997, the average number of fires that occurred in houses was 5,429 per year, the average number of deaths was 67 and the average dollar loss was $21,800 per fire. For this report, the Ontario fire statistics related to houses were analyzed to identify the fire scenarios that were associated with the greatest number of deaths per fire. The definition of a fire scenario includes the area of fire origin, the ignition source and the object first ignited. The identification of fatal fire scenarios will help identify the major fire concerns in houses and where future research efforts should be directed. In terms of future research effort, the frequent fatal fire scenarios that had the most deaths per fire can be used in fire simulations, either by fire modeling or experimentation, to determine why such fire scenarios have been so fatal. Such simulation studies will help identify fire protection measures that can be used in houses to improve fire safety. The Ontario fire statistics were used as they were readily available from the Office of the Fire Marshal. Also Ontario has one of the largest fire databases in Canada. It is assumed that the results of the present analysis can be applied to other houses in Canada.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".