iFAST: An Intelligent Fire-Threat Assessment and Size-up Technology for first responders
Bibliographic record
Abstract
Currently, emergency response agencies use simplified “one-size-fits-all” procedures to decide what quantity and type of resources to dispatch to each fire threat. These procedures are based on principles established decades ago, and are generally static in nature. They then rely on the judgment of the experienced officer who has arrived on-scene to make a dynamic evaluation and request additional units if appropriate. In this paper, we propose a fuzzy expert system (FES) to enhance the assessment procedures. The Intelligent Fire-Threat Assessment and Size-up Technology (iFAST) is shown to reduce the dispatch time (usually between eight to sixteen minutes) to less than 30 seconds; hence saving lives while reducing costs and property loss. The intent of the proposed system is to allow the emergency response agencies to perform the majority of the “initial-size-up” analysis in less than thirty seconds after a fire emergency report. Our system will outline the decisions in regards to the adequate resources that are required to be sent to the incident at the given time, as opposed to having to wait until the first experienced officer has arrived on-scene.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".