Analisis Selisih Perhitungan Muatan Kargo High Pressure Propylene Menggunakan Fishbone Analysis pada Industri Petrokimia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".