Critical Issues in Driving Simulation Methods and Measures
Bibliographic record
Abstract
This panel focuses on key issues that confront investigators who use driving simulators and must (a) design experiments, (b) interpret data and (c) write reports in human factors and medical research-related applications of these simulators. Presentations from an experienced panel of researchers from the U.S. and Canada aim to raise awareness of the audience (and panel members) and discuss solutions to a number of thorny research issues confronting driving simulator users. This effort draws upon but is distinct from previous work that has customarily emphasized engineering concerns facing simulator developers. The panel began with a “Collaboratory” at University of Iowa in March 2001. Discussions continued at the Driving Assessment conferences in Snowmass (2001) and in Park City (2003), the latter under the aegis of the Simulation Users Group (SUG). The SUG met again at the Transportation Research Board (TRB) in Washington, D.C. in January (2004) to address an array of topics including physical fidelity, simulation adaptation syndrome, standards for reporting methods, and variable selection. The session room was filled to capacity and the topics were deeper than could be addressed in the available time. Clearly, the discussions needed to continue beyond the TRB. The current HFES panel session addresses a limited set of topics in depth and allow the audience to explore the issues at length during the open discussion period. The abstracts of each panelist address topics that are not much discussed in the literature. These topics include: simulator discomfort related drop-out rates, participant characteristics, measurement precision and missing data, and the need for simulation standards, e.g., for reporting methodology and as a prelude to clinical trials, e.g., to test efficacy of “treatments” such as in-vehicle driver alerting devices for at-risk drivers.
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 | 0.768 | 0.859 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.011 | 0.012 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".