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Record W2131051069 · doi:10.1177/1071181312561462

A Driving Simulator Study Examining Phone Dialing with an iPhone vs. a Button Style Flip-Phone

2012· article· en· W2131051069 on OpenAlexaff
Bryan Reimer, Bruce Mehler, Birsen Donmez, Silviu Pala, Ying Wang, Nan Zaho, Kirsten Olson, John W. Wenzel, Joseph F. Coughlin

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2012
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Toronto
FundersU.S. Department of Transportation
KeywordsPhoneTask (project management)Interface (matter)Mobile phoneComputer scienceSimulationEngineeringTelecommunicationsOperating system

Abstract

fetched live from OpenAlex

A simulation study compared 36 young adult drivers’ task completion time, eye behavior, and driving performance while dialing a flip-phone with tactile pushbuttons and an iPhone which provides a touch screen interface. Participants who often use a traditional manual button phone completed the dialing task faster when using the flip-phone compared to touch screen users using the iPhone. Females using the flip phone had the highest percentage of time spent with eyes on the road. Females were also less likely to exhibit glances greater than 2 seconds in duration with both phone types and particularly with the flip-phone. Some advantages may exist in a traditional tactile manual interface in terms of the percentage of time drivers kept their eyes on the road.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.301
Teacher spread0.272 · 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.

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

Citations7
Published2012
Admission routes1
Has abstractyes

Explore more

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