CAMiCS: civilian activity modelling in constructive simulation
Bibliographic record
Abstract
When conducting operations in urban environments, military units often have to deal with ongoing civilian activity, including regular vehicle and pedestrian traffic, congregations and even crowds. Despite the important role played by civilian activity in the conduct of military operations, simulations used for military training often fail to represent civilian entities appropriately, both in terms of density and behaviours. This paper introduces a new tool (CAMiCS) that simulates vehicle traffic and pedestrian behaviour on the scale of a whole town, for training and experimentation at the operational level. CAMiCS is implemented as a multi-agent simulation and uses the current knowledge in human behaviour representation and traffic modelling. It can be used as a standalone simulation or in combination with other simulations on an HLA network. Terrain representation in CAMiCS is done using a geographical information system (GIS). Because of that feature, CAMiCS is especially well-suited for experimentation with GIS-based command and control systems. We present the fundamental models used for behaviour representation and traffic simulation, as well as the CAMiCS architecture. The benefits for training at the operational level are discussed.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".