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Record W3013331711 · doi:10.5383/juspn.08.01.002

Ambient Technology in Vehicles: The Risks and Benefits

2017· article· en· W3013331711 on OpenAlexvenueno aff
Ahmad Bennakhi, Maytham Safar

Bibliographic record

VenueJournal of Ubiquitous Systems and Pervasive Networks · 2017
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsnot available
Fundersnot available
KeywordsEmerging technologiesWork (physics)Computer securityComputer scienceInternet privacyEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper is aimed towards studying, compiling, and analyzing the recent advances and risks of the current ambient technology that is present in modern day cars. The progression of sophisticated technologies inside cars make them an even more comfortable and entertaining place to be in especially during commutes to work, but recent security threats and distractions have been uncovered with the upsurge usage of new technologies. The future of car’s interfaces is an issue since driverless cars are taking the scene by storm. This study also includes a survey that senses the people’s use of car technologies when driving.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.557

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.241
Teacher spread0.217 · 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 designSimulation or modeling
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
Published2017
Admission routes1
Has abstractyes

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