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Record W2909756248 · doi:10.1080/00140139.2019.1567829

Expert evaluation of traffic signs: conventional vs. alternative designs

2019· article· en· W2909756248 on OpenAlexaff
Tamar Ben-Bassat, David Shinar, Raquel Almqvist, Jeff K. Caird, R E Dewar, Esko Lehtonen, M. Sinclair, Heikki Summala, Lidia Żakowska, Gabriel Liberman

Bibliographic record

VenueErgonomics · 2019
Typearticle
Languageen
FieldPsychology
TopicSafety Warnings and Signage
Canadian institutionsThe Metabolomics Innovation CentreUniversity of Calgary
Fundersnot available
KeywordsHuman factors and ergonomicsComprehensionConventionApplied psychologySign (mathematics)Poison controlRisk analysis (engineering)EngineeringPsychologyComputer scienceMedicineEnvironmental health

Abstract

fetched live from OpenAlex

This study presents theoretical and methodological bases for evaluating the design of UN Conventional and alternative traffic signs. Human factors and ergonomics experts evaluated 31 conventional and 68 alternative road signs, based on ergonomics principles for sign design. Results indicated the need to re-examine poorly designed UN Convention signs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.086
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.086
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.078
GPT teacher head0.348
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2019
Admission routes1
Has abstractyes

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