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Record W4402695337 · doi:10.61132/jupiter.v2i5.561

Sistem Pendukung Keputusan Penentuan Kualitas Kayu untuk Kerajinan Mebel menggunakan Metode Electre Studi Kasus PT. Asia Mujur

2024· article· en· W4402695337 on OpenAlex
Andre Adrian, Rusmin Saragih, Magdalena Simanjuntak

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

VenueJupiter Publikasi Ilmu Keteknikan Industri Teknik Elektro dan Informatika · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsELECTREComputer scienceMathematicsOperations researchMultiple-criteria decision analysis

Abstract

fetched live from OpenAlex

Wood is the main element that determines the quality of a furniture product or other wooden crafts. Furniture was originally a wood carving craft industry, so that the furniture products produced emphasize the artistic aspect (carvings). The lack of knowledge of furniture companies and laypeople in this industry results in difficulties in determining the decision to choose wood to be used as a material for good and quality furniture crafts. Determining the quality of wood for furniture crafts needs to be strengthened by the increasing needs of the furniture industry. This industry has a high demand for quality wood raw materials to produce durable and aesthetic furniture products. Therefore, determining the quality of wood is crucial in ensuring the success of production and customer satisfaction..

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0010.000
Scholarly communication0.0100.017
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.236
Teacher spread0.218 · 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