MétaCan
Menu
Back to cohort
Record W3170894329 · doi:10.1049/pbhe026e_ch16

An extraocular muscle stimulation system based on EOG and FES

2020· book-chapter· en· W3170894329 on OpenAlexaff
Maram Arto, Aamnah Fannoush Alabboud, Fadi Al‐Turjman, İlker Özşahin, Dilber Uzun Ozsahin

Bibliographic record

VenueIET eBooks · 2020
Typebook-chapter
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsExtraocular musclesStimulationEye movementMedicinePhysical medicine and rehabilitationAnatomyOphthalmologyInternal medicine

Abstract

fetched live from OpenAlex

A device and system for treating misaligned eye positions sourced for any type of eye movement disease lead to checking for performance errors, by which the extraocular muscles or nerves are electrically stimulated when the eye movement of the controller's eye is detected non-invasively. The stimulation signal is sent to the electrode via a wireless system while the electrode is implanted in the stimulation selected area.

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)0.000
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 score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0210.007

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 head0.020
GPT teacher head0.224
Teacher spread0.204 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueIET eBooksSame topicGaze Tracking and Assistive TechnologyFrench-language works237,207