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Record W1913880346 · doi:10.1139/cjce-2014-0392

Psycho-physiological responses of drivers to road section types and elapsed driving time on a freeway

2015· article· en· W1913880346 on OpenAlexvenueno aff
Juyoung Kim, Jin‐Tae Kim, Wonchul Kim

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2015
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsnot available
FundersKorea Agency for Infrastructure Technology AdvancementMinistry of Land, Infrastructure and Transport
KeywordsWorkloadDriving simulatorSection (typography)Driving simulationElectroencephalographyTransport engineeringSimulationComputer sciencePsychologyEngineeringNeuroscience

Abstract

fetched live from OpenAlex

This paper addresses drivers’ psycho-physiological condition under the influence of various freeway section types and elapsed driving times. The authors analyzed the electroencephalogram (EEG) signals (α, β, and θ) of 51 drivers on a freeway in Korea. The findings show that the driver’s workload increases in tunnels and on left-curved sections, and that his or her concentration and response ability decrease after 60 min of elapsed driving time. The β/α ratios of EEG signals were found to be most effective in detecting differences in psycho-physiological responses. The results can help to promote safety on freeways by encouraging drivers to take rests every hour.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.834
Threshold uncertainty score1.000

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)0.0010.000

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 head0.024
GPT teacher head0.284
Teacher spread0.260 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

Explore more

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