The Atomic Model approach to operational assurance
Bibliographic record
Abstract
There is an inherent risk in transitioning project personnel out and transitioning the incoming operating crew in with minimal disruption to operations and ensuring the integrity of the design. The key to mitigating this risk is to execute operational readiness assurance (ORA) processes, which provide confidence that a facility has seamless transition from design into operation, and that production targets are met in the first years of the asset’s life. It is a rigorous process spanning engineering, construction, commissioning, handover, and operations; this ensures confidence in operation and asset life cost. This extended abstract focuses on a proven methodology to this aspect; it is highlighted using the Atomic Model approach, which is being implemented on the Wheatstone Processing Platform for Chevron. The atomic model consists of a nucleus (operability, reliability, and maintainability [ORM]) that provides the positive charge that will hold the whole process together; it also presents a consistent and operable asset to the operations and maintenance crew. The electrons—being the component parts—include competency, process safety, process control, operating and maintenance procedures, and maintenance management system. Together, these fully ensure operability. The enemy of ORM is complexity and the Atomic Model is a systematic approach to this complexity. Implemented by a team that views the project holistically, this approach produces a facility that is operated and maintained in a uniform manner. This consistent and disciplined approach to operations, maintenance, and reliability is the positive charge needed in the nucleus for the path towards seamless start-up.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".