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Record W2058723972 · doi:10.1145/2470654.2466463

CrashAlert

2013· article· en· W2058723972 on OpenAlex
Juan David Hincapié-Ramos, Pourang Irani

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceAlertnessHuman–computer interactionPedestrianMobile deviceMobile interactionPath (computing)Computer visionArtificial intelligenceEngineeringWorld Wide WebPsychologyComputer network

Abstract

fetched live from OpenAlex

Mobile device use while walking, or eyes-busy mobile interaction, is a leading cause of life-threatening pedestrian collisions. We introduce CrashAlert, a system that augments mobile devices with a depth camera, to provide distance and location visual cues of obstacles on the user's path. In a realistic environment outside the lab, CrashAlert users improve their handling of potential collisions, dodging and slowing down for simple ones while lifting their head in more complex situations. Qualitative results outline the value of extending users' peripheral alertness in eyes-busy mobile interaction through non-intrusive depth cues, as used in CrashAlert. We present the design features of our system and lessons learned from our evaluation.

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.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.649
Threshold uncertainty score0.845

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)0.6710.156

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 head0.025
GPT teacher head0.362
Teacher spread0.337 · 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

Quick stats

Citations47
Published2013
Admission routes1
Has abstractyes

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