Bibliographic record
Abstract
Abstract The evolution of simulators from proprietary hardware platforms to affordable commercial-off-the-shelf (COTS) platforms has gone on for the past 15 years and is now nearly complete. Nevertheless, past efforts to standardise simulator synthetic environments (SE) have only been partially successful and have engendered considerable aggravation for users in need of creating content that can be deployed to distributed full-mission simulators. This paper provides a detailed description of the SE generation pipeline and the reasoning that has modeled its evolution over the past few decades. The arrival of digital multi-spectral high-resolution satellite imagery and highly capable visual systems now requires orders of magnitude more storage and processing than equivalent databases just a few years ago. These factors are threatening the equilibrium of the SE pipeline and are becoming important elements affecting SE interoperability, portability and re-usability. Past design trade-offs and compromises, appropriate at the time, must now be re-examined along with all SE-related processes, starting from ingestion of raw source data right through to the processing by the simulator devices. Clearly, greater standardisation is needed within the simulation community and a comprehensive, open SE representation would palliate to the many challenges we now face. To this end, this paper provides a checklist of the characteristics for a future ‘ideal’ SE representation and evaluates four emerging synthetic environment initiatives against this extensive checklist.
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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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".