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Record W4389745758 · doi:10.15578/chanos.v21i1.12772

PROCESS CAPABILITY ANALYSIS OF KATSUOBUSHI PRODUCTION AT PT. ABC

2023· article· en· W4389745758 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

VenueChanos Chanos · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsSix SigmaProcess (computing)Process capabilityProduction (economics)Computer scienceValue (mathematics)Manufacturing engineeringReliability engineeringProcess managementEngineeringRisk analysis (engineering)Work in processOperations managementBusinessLean manufacturing

Abstract

fetched live from OpenAlex

Process capability in processing fishery products is a future challenge for the fishing industry, one of which is katsuobushi. The capability process refers to how a company can produce according to specifications to make it effective and efficient. The absence of process capability in the company is the basis for stating an effective and efficient process. The proposed study is to determine the company's current condition and provide suggestions for process improvement and evaluation. Through assumptions, this study influences the results of Cp and Cpk, which are calculated by Minitab software version 21st as a method for measuring performance by identifying broad process tolerance production. In this study, the results for each data generated met data accuracy and standards. Still, at smoking 3rd and 4th stages, it was necessary to review specification values because of the value of Cpk < 1.00. As a result, after improving the standard by the six sigma concept, Cpk values are obtained > 1.00.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.259
Teacher spread0.231 · 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