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Record W4387377094 · doi:10.59934/jaiea.v3i1.326

Diagnosing a Paper Dryer Malfunction in PT.Bamindo Agrapersada Using the Method DecisionTree

2023· article· en· W4387377094 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

VenueJournal of Artificial Intelligence and Engineering Applications (JAIEA) · 2023
Typearticle
Languageen
FieldComputer Science
TopicData Mining and Machine Learning Applications
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsDecision treeComputer scienceTree (set theory)Field (mathematics)Outcome (game theory)Production (economics)Operations researchArtificial intelligenceMachine learningEngineeringMathematicsEconomicsMathematical economics

Abstract

fetched live from OpenAlex

PT. BAMINDO AGRAPERSADA is a private company that processes bamboo into paper and sells it to consumers. In an industrial field, problems often occur to overcome damage to paper drying machines because mechanics are difficult to find and sometimes time is not possible so that operations are hampered while production targets must be completed immediately. Decision Tree which is a tree that is used as a reasoning procedure to get answers to the problems entered. Decision trees provide a way to present algorithms with conditional control statements that include branches in decision-making steps that lead to a favorable outcome. With the Decission Tree, it can minimize damage to the paper drying machine and help solve problems in the PT. BAMINDO AGRAPERSADA industry.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.774
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.043
GPT teacher head0.324
Teacher spread0.281 · 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