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Record W4387878391 · doi:10.61306/jnastek.v3i4.104

UTILIZATION OF THE CERTAINTY FACTOR METHOD TO DIAGNOSE EYE DISEASES

2023· article· en· W4387878391 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJurnal Nasional Teknologi Komputer · 2023
Typearticle
Languageen
FieldComputer Science
TopicEdcuational Technology Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsCataractsOptometryMacular degenerationMedicineStaringAccommodationPopulationOphthalmologyDiabetic retinopathyGlaucomaTrachomaEye diseaseBlindnessRefractive errorPresbyopiaPsychologyDiabetes mellitusEnvironmental health

Abstract

fetched live from OpenAlex

In Indonesia, the number of people with eye disease increases every year. The blindness rate of the population in Indonesia is around 1.2% of the total population. People with this eye disease have problems ranging from mild to blind spots. The main causes of blindness are cataracts, corneal disorders, glaucoma, refractive errors, dry eyes, retinal disorders and nutritional disorders. Conjunctivitis, macular degeneration, diabetic retinopathy and other diseases that affect the eyes. As vision, it is necessary to keep the function of the eye from decreasing. As a person ages, the accommodation ability of the eye also decreases. The causes include sitting too long in front of a computer or staring at a cellphone screen for too long, reading a book at a distance that is not within a normal healthy distance, or the presence of dirty air and solar radiation.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.623
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.054
GPT teacher head0.343
Teacher spread0.289 · 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