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Record W2795438861 · doi:10.1145/3170427.3180306

Developing a Typeface for Low Resolution E-Ink Displays

2018· article· en· W2795438861 on OpenAlexaff
Benjamin C. Smith, Terra David Groenewold

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsTypefaceLegibilityFontInkwellComputer scienceSchematicTypographyReading (process)Computer graphics (images)Artificial intelligenceComputer visionEngineeringArtSpeech recognitionVisual artsLinguistics

Abstract

fetched live from OpenAlex

For use on E-Ink displays, a typeface is required to convey information. Whether that information be stories to data, legibility of the information is important. E-ink Displays require a difference in typeface usage due to their lack of color as well as low-resolution capabilities in comparison to modern day displays. We developed two typefaces to assess the quality of legibility for use on E-ink displays in an educational or business environment, using the typeface Arial as a standard. Legibility is measured in terms of how many correct words the subject reads aloud and the time taken to initially speak. Time is not used to assess reading speeds, but to establish difficulty in legibility. Three different font sizes are used to determine any lower bound thresholds on legibility that exist within the typeface. These methods allow for a font to be developed and used with E-ink displays for clear communication.

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)0.005
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0110.005

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 head0.031
GPT teacher head0.350
Teacher spread0.318 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations5
Published2018
Admission routes1
Has abstractyes

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