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Record W4383047380 · doi:10.54314/teknisi.v1i2.660

SISTEM PAKAR MENDIAGNOSA PENYAKIT TANAMAN MANGGA ARUMANIS DENGAN METODE CERTAINTY FACTOR

2021· article· en· W4383047380 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 TEKNISI · 2021
Typearticle
Languageen
FieldComputer Science
TopicInformation Retrieval and Data Mining
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsAgricultureCropValue (mathematics)Computer scienceGeographyForestryMachine learning

Abstract

fetched live from OpenAlex

Diseases that attack mango plants cause mango farmers to suffer losses due to damaged crops or crop failure. To assist agricultural extension workers in diagnosing temporary diseasesthat attack mango arumanis plants if agricultural experts or experts do not exist or do not exist, a computer-based system is needed that can provide information on diseases and how to control it. This study builds a rule-based expert system using the cairtanty factor method to diagnose diseases in mango arumanis plants based on the symptoms of each diseases. The results obtained are in the form of diseases that attack and control procedures that attack and control procedures that must be carried out, as well as the certanty factor value of the truth of the diseases.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

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 communication0.0010.001
Open science0.0010.000
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 head0.022
GPT teacher head0.251
Teacher spread0.229 · 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