Distributed Mission Training - How Distributed Should It Be
Bibliographic record
Abstract
The UK's MoD has funded a programme of applied research to explore the benefits to be gained from using networks of simulator, or Synthetic Training Environments (STEs), for multi-role air mission training (i.e collective training). Within the UK the use of networked simulation in this context has become known as Mission Training through Distributed Simulation (MTDS). Within the US it is known as Distributed Mission training (DMT). The Defence and Science Technology Laboratory (Dstl) and QinetiQ have undertaken the MoD sponsored research via a series of trials. The trials have been conducted under the banner heading of RAPTORS3. To date four trials have taken place; Ebb and Flow, SyCOE, VirtEgo and SyCLONE. All have been conducted using the synthetic Composite Air Operation (COMAO) test-bed created specifically to assess the potential of MTDS. Combat-ready, front-line aircrew and an expert White Force from the UK's Air Warfare Centre (AWC) Tactical Wing and Training have participated in all four trials. The research has indicated that there is, potentially, much to be gained from the use of networked simulation for MTDS. The question remains as to the extent that participants should or could be distributed during MTDS exercises. This is particularly pertinent if the aspiration is to use networked simulation for coalition training, because, of necessity this would require some training participants to be geographically dispersed. The last two trials therefore included a Wide Area Network (WAN) to link together research facilities in Canada, the UK and US. This paper will discuss the outcome of these trials with particular reference to SyCLONE.
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.014 | 0.030 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".