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Record W4412878847 · doi:10.54082/jupin.1526

Analisis Selisih Perhitungan Muatan Kargo High Pressure Propylene Menggunakan Fishbone Analysis pada Industri Petrokimia

2025· article· id· W4412878847 on OpenAlexaff
Rohiman Ahmad Zulkipli, Ferry Ikhsandy, Muhammad Yasyfi Yahdiyan

Bibliographic record

VenueJurnal Penelitian Inovatif · 2025
Typearticle
Languageid
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsHigh pressureEngineering

Abstract

fetched live from OpenAlex

Selisih perhitungan muatan antara angka kapal (ship figure) dan angka darat (shore figure) kerap terjadi dalam proses bongkar muat di industri petrokimia, khususnya pada penanganan kargo High Pressure Propylene. Ketidaksesuaian ini dapat menimbulkan ketidakefisienan operasional serta potensi kerugian finansial. Penelitian ini bertujuan untuk menganalisis perbedaan kuantitas muatan selama proses discharge dan mengidentifikasi strategi untuk meminimalkan selisih tersebut. Metode yang digunakan adalah metode kuantitatif dengan membandingkan data ship figure dan shore figure dari aktivitas bongkar muat. Hasil penelitian menunjukkan adanya selisih sebesar 6,388 metrik ton. Temuan ini menekankan pentingnya pengambilan data yang akurat, pengendalian tekanan, serta kestabilan kapal selama proses bongkar, guna meningkatkan ketepatan pengukuran dan menjamin keandalan proses transfer kargo di industri petrokimia.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.007
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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