Bibliographic record
Abstract
A little over 10 years ago, Chevron developed and implemented an Asset Reliability Process with the goal of improving the reliability of our facilities, with a strong focus on improving the availability of our facilities. This was known as URIP or our Unit Reliability Improvement Process. The building blocks of this process include subprocedures for: Design for reliability Reliability opportunity identification and resolution Risk assessment and asset strategy Surveillance and condition monitoring Proactive maintenance Maintenance and failure prevention While we were successful in improving the availability of our facilities with the implementation of URIP, we continued to experience incidents and unplanned downtime associated with the integrity of our assets. As a result, we identified the need to revise our process to place a greater focus on asset integrity. This presentation describes the development and implementation of our revised reliability process, which we have renamed as our Unit Reliability and Integrity Process. The resulting asset care program still includes the subprocedures listed above, with the incorporation of asset integrity requirements. © 2018 American Institute of Chemical Engineers Process Saf Prog 38: e12018, 2019
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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 | 0.059 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".