Universal Simulation Platform Online, A Multi-Participants I.E.D. Disposal Simulator
Bibliographic record
Abstract
Improvised explosive devices are unconventional weapons that are a significant threat to public safety. Explosive ordnance disposal personnel are tasked with determining methods and procedures for locating, neutralizing, and disposing of chemical, biological, radiological, nuclear and explosive threats in extreme environments. Training methods vary depending on the region and military unit, and may use a combination of tools to facilitate a learning process. In our previous work, we proposed a Virtual Reality simulation called Universal Simulation Platform to enable explosive ordnance disposal personnel to train in a safe virtual environment without being exposed to potentially harmful real-world situations whilst being cost effective. In this paper, we propose a multi-participant revision of the simulation software, which allows multiple participants to connect and join a session, work together and attempt to neutralize a bomb(s). A multi-participant improvised explosive device disposal simulation is a novel virtual reality training approach which introduces crucial neutralization training aspects such as teamwork and communication, often neglected functions of virtual reality training simulations.
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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 | 0.003 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".