Consideration of reliability and performance of fire protection systems in FiRECAM<TM>
Bibliographic record
Abstract
Reliability and performance of fire protection systems in a building are important considerations in the new objective/performance-based code environment. An increased reliability and performance would provide a higher level of life safety to the occupants. To help designers and building officials assess the impact of reliability and performance of fire protection systems, assessment tools are essential. In this paper, the computer fire risk-cost assessment model that is being developed at the National Research Council ofCanada (NRC) is used to show, as an example, how the impact of reliability of fire alarms and automatic sprinklers on life safety in a building can be quantitatively assessed. The NRC model is called FiRECAM (Fire Risk Evaluation and Cost Assessment Model). FiRECAM evaluates the life risks to the occupants and fire costs as a result of all probable fires in a building and by simulating the dynamic interaction of fire growth, smoke movement, occupant response and fire department intervention. These interactions are affected by the reliability of fire alarms and automatic sprinklers, as well as the performance of other fire protection systems that are installed.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".