MétaCan
Menu
Back to cohort
Record W1547255040

Determining the Appropriate Font Size, and Use of Colour and Contrast for Underwater Displays

2008· article· en· W1547255040 on OpenAlexaboutno aff
J. B. Morrison, J. Zander

Bibliographic record

VenueDefense Technical Information Center (DTIC) · 2008
Typearticle
Languageen
FieldPsychology
TopicSafety Warnings and Signage
Canadian institutionsnot available
Fundersnot available
KeywordsFontUnderwaterReadabilityContrast (vision)Computer scienceComputer visionArtificial intelligenceComputer graphics (images)Geography
DOInot available

Abstract

fetched live from OpenAlex

Canadian mine countermeasures (MCM) divers currently use a combination of different displays to provide them with information about their safety, equipment, and status. MCM divers require a single, integrated display to simplify information gathering, and to provide information from the dive supervisor. Ergonomic design guidelines were reviewed and modified for information display in the underwater environment. A two-phase experiment was conducted to determine the optimal font size and the optimal colour, contrast, and background combination(s) for underwater displays. Eighteen subjects viewed a series of displays in four environments that simulated a combination of light and dark, clear and turbid conditions. Each subject viewed over 210 display screens to compare different font sizes and colour and contrast combinations. Each screen was scored for accuracy and readability. Results showed that when designing an underwater display, character height should be approximately 6 mm (26 point font size) when using Arial font. The display should have a black (or dark) background with light foreground letters. Light orange or light green were found to be the optimum colours for use in the display. A set of ergonomic guidelines for the design of underwater displays were developed based on the results of this study.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.353

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)0.0000.000

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 head0.042
GPT teacher head0.276
Teacher spread0.234 · 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 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

Citations2
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueDefense Technical Information Center (DTIC)Same topicSafety Warnings and SignageFrench-language works237,207