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Record W2031285578 · doi:10.1145/1806923.1806925

SAK

2010· article· en· W2031285578 on OpenAlexaff
I. Scott MacKenzie, Torsten Felzer

Bibliographic record

VenueACM Transactions on Computer-Human Interaction · 2010
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsYork University
FundersDeutsche Forschungsgemeinschaft
KeywordsBlock (permutation group theory)Key (lock)Computer scienceCharacter (mathematics)Interval (graph theory)Feature (linguistics)SoftwareMathematicsComputer securityOperating systemLinguistics

Abstract

fetched live from OpenAlex

The design and evaluation of a scanning ambiguous keyboard (SAK) is presented. SAK combines the most demanding requirement of a scanning keyboard—input using one key or switch—with the most appealing feature of an ambiguous keyboard—one key press per letter. The optimal design requires just 1.713 scan steps per character for English text entry. In a provisional evaluation, 12 able-bodied participants each entered 5 blocks of text with the scanning interval decreasing from 1100 ms initially to 700 ms at the end. The average text entry rate in the 5 th block was 5.11 wpm with 99% accuracy. One participant performed an additional five blocks of trials and reached an average speed of 9.28 wpm on the 10 th block. Afterwards, the usefulness of the approach for persons with severe physical disabilities was shown in a case study with a software implementation of the idea explicitly adapted for that target community.

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)0.002
metaresearch head score (Gemma)0.006
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: Other · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0240.009

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 head0.046
GPT teacher head0.321
Teacher spread0.275 · 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
GenreOther

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

Citations75
Published2010
Admission routes1
Has abstractyes

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Same venueACM Transactions on Computer-Human InteractionSame topicTactile and Sensory InteractionsFrench-language works237,207