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Record W4294578417 · doi:10.3233/shti220863

Unidirectional Tactile Paving: Circulation for the Visually Impaired

2022· article· en· W4294578417 on OpenAlexaff
Juan Fernández González, Ankit Gongal

Bibliographic record

VenueStudies in health technology and informatics · 2022
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsMcGill University
Fundersnot available
KeywordsLimitingCirculation (fluid dynamics)Visually impairedDistancingComputer scienceCoronavirus disease 2019 (COVID-19)Social distanceHuman–computer interactionCommunicationEngineeringPsychologyMechanical engineeringMedicineAerospace engineering

Abstract

fetched live from OpenAlex

The COVID-19 pandemic heightened inequalities of universal accessibility in the built environment. The visually impaired have been affected by the lack of mobility that resulted from social distancing and physical distancing. Numerous circulation networks, from small retail shops to large-scale public spaces, have been reorganized to limit the spread of COVID-19. By assigning an orientation to each lane, unidirectional circulation networks allow for physical distancing by limiting face-to-face interactions among most pedestrians. These unidirectional networks are communicated visually, by placing flat arrows on the floor, but not through the other senses and are thus inaccessible to the visually impaired. This demonstrates a lack of universally accessible design for unidirectional circulation. We propose two designs (asymmetrical blocks and cobblestone blocks) for a novel unidirectional tactile paving which allows the visually impaired to navigate through unidirectional circulation networks by feeling tiles with their feet and/or canes.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.036
GPT teacher head0.318
Teacher spread0.282 · 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 designNot applicable
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

Citations3
Published2022
Admission routes1
Has abstractyes

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