Enhancing data visualisation to capture the simulator sickness phenomenon: On the usefulness of radar charts
Bibliographic record
Abstract
" (Chaumillon et al., 2017) [1]. In an outstanding first demonstration, Kennedy et al. [2] showed that the Simulator Sickness Questionnaire (SSQ) is an appropriate tool to suit the purposes of characterizing motion sickness experienced in virtual environments. This questionnaire has since been used in many scientific studies. Recently, Balk et al. [3] suggested that the proposed segregation of SSQ scores into three subclasses of symptoms might limit the accuracy of simulator sickness assessment. These authors performed a factor analysis based on SSQ scores obtained from nine studies on driving simulators. Although their factor analysis resulted in the same three orthogonal classes of symptoms as Kennedy et al. [2], unlike this pioneering study, no items were attributed to more than one factor and five items were not attributed to any class of symptoms. As a result, they claimed that an exploration of each item score should give additional cues on individual profiles. To gain a better characterization of such item-by-item exploration, data utilised in this research are shown using a radar chart visualisation.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".