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Record W3005539563

SISTEM PAKAR MENDIAGNOSA PENYAKIT GIGI MENGGUNAKAN METODE CERTAINTY FACTOR

2020· article· id· W3005539563 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

Venuenot available
Typearticle
Languageid
FieldComputer Science
TopicEdcuational Technology Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsGingivitisMedicineDentistryPeriodontitisExpert systemPeriodontal diseaseComputer scienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Expert system is a computer-based application that is used to solve problems as thought by experts. One problem that can be solved by using an expert system is diagnosing.Teeth are one of the most important organs in the human body. As the only organ that cannot heal itself, the tooth becomes an organ that is highly maintained and cared for during one's life. The teeth are also very important organs in the food processing process. In this study the user chose one of three types of dental disease as their assumptions including periodontitis, gingivitis and dental caries. The system will give questions about the symptoms of dental disease. The certainty factor value of the three types of dental diseases based on user input is 95.9% of gingivitis, 92.7% of periodontitis and 85.9% of dental caries. So it can be concluded that the calculation results indicate the likelihood of users suffering from gingivitis 95.9%. Henceforth the system will provide a handling solution.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.005

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 head0.062
GPT teacher head0.269
Teacher spread0.208 · 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

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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