First Responder Immersive Training Simulation Environment (FRITSE): Downwind Hazard Modeling of Scenarios
Bibliographic record
Abstract
Abstract : In the Chemical, Biological, Radiological-Nuclear and Explosives Research and Technology Initiative(CRTI) Project 09-509TD, a persistent, highly realistic, game-based synthetic environment (SE) for incident commanders and first responders to conduct individual and/or team training and collaboration (at the tactical and/or strategic levels) to address deliberate or accidental Chemical, Biological, Radiological, Nuclear and Explosives (CBRNE) releases in an urban environment has been proposed. This will be achieved by integrating a state-of-the-science physics-based urban flow/dispersion modeling system and a high-fidelity building-aware Geographic Information System (GIS) representation of a real cityscape (Calgary) with a proven game-based simulation engine developed by 3DInternet Inc. WATCFD is responsible for the provision of hazardous plume entities to the game-based simulation engine. The objective of this report is to provide a technical description of modeling and simulation of the highly disturbed building-induced flow field in downtown Calgary and the transport and dispersion of hazardous agents released into this complex flow field using the computational fluid dynamics (CFD) model urbanSTREAM. It is envisaged that the development of the game-based virtual reality CBRNE training environment developed in the present project will provide the highest quality training to incident commanders and first responders, allowing the end user to experience and respond to realistic high-risk scenarios involving CBRNE hazards in a complex cityscape in a completely safe and controlled environment.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".