A Simulation Algorithm Capable Of Modelling Spatial Impact Points From The Neutralization Of An Improvised Explosive Device
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An improvised explosive device (IED) is a bomb constructed from unknown materials, often concealed, such as inside an innocuous container, and deployed in unconventional ways resulting in a potentially deadly weapon. Public safety personnel such as Explosive Disposal Units (EDUs), are trained in the safe handling of explosives and the threats posed by IEDs. One method of neutralizing a suspect IED is to use water fired from a high-powered dispersion weapon commonly known as a disrupter cannon. Our research proposes an algorithm for developing an IED neutralization simulation that can emulate real-world physical effects of the successful neutralization of an IED without danger to the public or first responders. This algorithm includes 6 methodologies with the goal of providing EDU with additional information on the potential physical dispersion of the components of an IED and any major points of impact (splatter) and possible actionable intelligence on the pose and direction of a disrupter cannon for a successful neutralization of an IED. We have developed a prototype simulation based on this algorithm and evaluated the simulation with an appropriate real-world disrupter and compared the real-world splatter to our simulation’s splatter. We argue systems developed with our algorithm may provide relevant information directly from the simulation and can be accurately used to analyze particle dispersion for the purposes of augmenting EDU IED neutralization processes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it