VCELL: a 3D real-time visual simulation in support of combat
Bibliographic record
Abstract
We present the development of a 3D real-time visual Cellular Agent model (VCELL). VCELL is used for simulating land combat and is collaboratively modeled using a cellular agent model based on the Cell-DEVS formalism and an advanced immersive environment based on a 3D real-time visual simulation. VCELL is used to enhance and improve the random selection caused by movement algorithms of Agentbased distillation (ABD). The model includes a highly modular collection of software packages designed to facilitate the development of device-independent simulation for land combat. The immersive environment is used to visualize the land combat. The simulation results of the Cell-DEVS agent model are visualized dynamically in real-time. The goal is to show how to integrate cellular modeling in a real-time platform and 3D real-time visualization as a collaboration mechanism to enhance movement algorithms in land combat. The 3D real-time visualization allows for supervisory control of the land combat activities. 1.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".