Crossing the barrier : a scalable simulator for course of fire training
Bibliographic record
Abstract
The growing training and operational needs of law enforcement and public safety personnel can no longer be met efficiently and effectively through existing infrastructure and resources. While the demands of day-to-day operations are constantly changing, the training of law enforcement personnel and certification process has largely stayed the same. While several technological solutions exist for enhancing training, widespread adoption of current approaches and solutions, such as virtual training, is hindered by significant cost barriers and by lack of scalability and reach. This creates challenges for smaller geographically disconnected units, which characterizes most rural police departments in North America. This paper presents MINT-PD, a technological solution for multimodal virtual Course of Fire (COF) training, along with field observations and validation of the technology. One specific application of MINT-PD is to help to increase the rate of success among the trainees who failed a first COF certification round. Success in this context represents significant cost savings by reducing active-duty officers' down-time due to the remedial training and reducing the need for the use of the live firing range. MINT-PD is based on the Multimodal Interactive Trainer (MINT) simulation platform developed by NRC, specifically adapted to address user needs in training and certification for a typical municipal police department. MINT-PD technology allows users to modify training scenarios, incorporate different types of laser guns and a flashlight, add avatars, and expand the training to include Use of Force scenarios. The conditions and parameters implemented in the virtual COF simulator have been derived from field observations and validated by COF trainers within a medium-size police department. This process of technology development and validation will be described in the paper.
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 | 0.000 | 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".