Advancing Simulation Reusability - Report on NATO MSG-042 Findings
Bibliographic record
Abstract
In many cases, the training and decision support needs of military users are urgent; operations cannot wait and missions have to be accomplished. Simulators, wargames scenarios and experiments should be ready 'yesterday'. New kinds of operations, environments, tactics, equipment and force configurations challenge our simulation capacities. To mitigate the cost impact and meet the time sensitive requirements, the M&S community has to be 'ready in advance'. This objective may be brought nearer by reusing resources that have been previously developed, possibly by external organizations, and reconfiguring and assembling these resources according to the current needs. Today, more than ever, warfighting excellence is related to the level of reusability of M&S resources. The NATO Modelling and Simulation Task Group MSG-042 (part of the NATO Research and Technology Organization, RTO) is focused on fostering simulation resource reusability within NATO and partners. Seven nations (Canada, Germany, France, The Netherlands, Spain, UK and USA) participate in this effort. MSG-042 is studying and analysing the factors that can enable a shared and common framework in which reuse of modelling and simulation resources will be supported. Our focus is not only on technical issues but also on organizational and cultural aspects that, as we have discovered, have a great impact on the capability of sharing resources, especially at multinational level.MSG-042 recommendations will address three different aspects: reusability actors (Authorities, Producers, Consumers and Custodians), resources (any kind of item useful for simulation) and repositories (containers of resources). MSG-042 will also recommend a common architecture for connecting repositories and sharing resources.
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 | 0.024 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".