Bibliographic record
Abstract
Enroute air traffic control (ATC) relies heavily on simulation in training, research, and concept development applications. However, it has little domain-specific research on the effects of simulation fidelity and lacks a standardized definition of simulation fidelity in the literature. A survey of ATC industry professionals shows that simulation fidelity is not perceived to be well defined for the domain of enroute ATC, regardless of respondent nationality, experience, use of simulation or gender. Parts of the operational environment that survey respondents felt were important components in a definition of simulation fidelity are reported; Communications is the most important component regardless of nationality, experience, use of simulation or gender. Implications for the development of a standardized definition of simulation fidelity are discussed. Simulation fidelity has been researched and investigated for over half a century, yet it remains a somewhat nebulous concept today. The high-level concept of simulation fidelity can be best understood from a definition posited by Hays and Singer: “Simulation fidelity is the degree of similarity between the training situation and the operational situation which is simulated (1989, p. 50).” While this definition is intuitive, more detail is needed for operational applications such as determining the most effective simulation environments for training. Hays and Singer have also provided a more comprehensive definition: “Simulation fidelity is the degree of similarity between the training situation and the operational situation which is simulated. It is a two dimensional measurement of this similarity in terms of: (1) the physical characteristics, for example, visual, spatial, kinesthetic, [auditory], etc.; and (2) the functional characteristics, for example the informational, and stimulus and response options of the training situation (1989, p.50).”
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".