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Record W2887779540

Comprehensive assessment of simulator vehicle parameters and upper body kinematics and muscle activation between two display modalities in a driving simulator

2018· dissertation· en· W2887779540 on OpenAlexfundno aff
Theresa-Lynne Filio

Bibliographic record

VenueThe Atrium (University of Guelph) · 2018
Typedissertation
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSimulationKinematicsDriving simulatorComputer scienceModalitiesPhysical medicine and rehabilitationMedicinePhysics
DOInot available

Abstract

fetched live from OpenAlex

Driving simulators in hazard response studies provide safer environments for participants than naturalistic driving studies. Many driving simulator visual cueing systems are comprised of a set of wrap around screens (WASs) that when increased in number and size can enhance the realism of the simulation, creating a driving experience closer to naturalistic driving. This, however, can be costly. Recent designs of head mounted display (HMD) technology are low-cost making them a potential alternative, however, their effect on hazard response results in comparison to WASs is untested. Driver responses were compared between WASs and an HMD during an unanticipated pedestrian crossing. Perception-response times were significantly greater with the HMD, highlighting the importance of understanding the effects display modality may have on results. In contrast, many of the physiological variables did not exhibit significant differences between the display modalities suggesting that HMD specifications may not have a large impact on physiological responses.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.298
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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