University of Waterloo Live Fire Research Facility
Bibliographic record
Abstract
Every year in North America, fire accounts for a significant number of deaths, injuries, and capital losses, as well as the related economic burdens of fire insurance, fire suppression and building fire protection systems. Indirect costs of fire due to emissions to the environment and groundwater contamination are immeasurable. These costs can be reduced through the development of fire-safe products, advancements in detection, suppression and fire fighting methodologies, and the development of innovative equipment for the fire protection and response industries. With this aim, University of Waterloo researchers from the Departments of Mechanical Engineering and Applied Health Sciences, together with the fire service in their region and other municipalities across Canada, have conducted collaborative fire training and research exercises for over a decade. A unique synergy has developed, leading to direct transfer of technology to the broader fire service community; however, it is very difficult to access facilities in which controlled, realistic, large-scale, live-fire research can be conducted. To address this need, a $5.6M, state-of-the-art Live Fire Research Facility is being constructed by the University of Waterloo with shared funding from the Region of Waterloo, the City of Kitchener, the Canadian federal and Ontario provincial governments and industry. The research facility is an integral part of a larger Region of Waterloo Emergency Services Training and Research Complex, situated on a 16 ha. site, minutes from the UW campus.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.252 | 0.051 |
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".