Penentuan Tingkat Kritikalitas Peralatan Pembangkit Dengan Metode Equipment Criticality Management Dalam Rangka Penentuan Prioritas Pemeliharaan
Bibliographic record
Abstract
The criticality level of equipment used at PT PLN (Pesero) power plants at present is using the Maintenance Priority Index (MPI) method. The calculation for the criticality rating of MPI equipment uses 4 (four) types of calculations, namely SCR, OCR, ACR and AFPF. To add to the consideration in determining the priority of equipment maintenance, an additional calculation of the criticality level of PLTU Tarahan equipment is carried out using the Equipment Criticality Management method. The Equipment Criticality Management method has 4 (four) assessment perspectives, namely Production, Safety, Environment and Equipment Failure. Calculations that have been carried out on the top 100 (one hundred) equipment in the PLTU Tarahan SERP using the Equipment Criticality Management method, there are 85 (eight five) equipment that has “High” criticality and 15 (fifteen) equipment in the “Medium” criticality category. 15 (fifteen) equipment that has “Medium” criticality is equipment that has backup and part of common generating equipment.
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 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".