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Record W4387376897 · doi:10.59934/jaiea.v3i2.298

Determination of Priority for Recipients of Distribution Assistance Facility IKM Business Actors (Small and Medium Industries) Using the Moora Method in Langkat District

2023· article· en· W4387376897 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
FieldBusiness, Management and Accounting
TopicDecision Support System Applications
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsBusinessSample (material)Operations managementDistribution (mathematics)Rank (graph theory)Production (economics)Capital (architecture)EngineeringMathematicsEconomics

Abstract

fetched live from OpenAlex

To determine priority recipients of assistance facilities for IKM business actors in Langkat Regency, the criteria used in this study are the type of production, capital requirements, sales results, number of employees, and length of business. The data used in this study comes from the Department of Trade and Industry. In this study the MOORA method was used which aims to design and build a decision support system in determining priority recipients of facilities for IKM business actors. The author wants to make this decision support system using the Visual Basic application and supported by the MySQL Database so that the results are more effective and efficient. From the 10 sample data obtained by means of prioritizing recipients of assistance facilities for IKM business actors, it can be seen that those who get rank I with the highest score i.e. 0.1133 is A06 in the name of devi.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.783
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.084
GPT teacher head0.325
Teacher spread0.241 · 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