Bibliographic record
Abstract
Safety is a core value at Esso Australia. We strive to observe the highest standards of safety to ensure that nobody gets hurt in our operations. We believe this goal can be achieved through a broadly shared commitment to personal and process safety—both of which are managed using our operations integrity management system (OIMS). In the Gippsland region of Victoria, Esso Australia operates oil and gas production facilities ranging from sub-sea completions to substantial staffed offshore facilities, an onshore crude stabilisation, three gas processing plants and a natural gas liquids fractionation plant, all interconnected by a network of offshore and onshore pipelines. Everyday Esso’s Gippsland operations produce millions of litres of crude oil and millions of cubic meters of natural gas. Having all this fuel energy flowing through these plants each day at high pressures, and widely ranging temperatures, it is imperative that it is safely controlled and contained by the process equipment. How do we do this? With process safety systems. Process safety is a crucial component of OIMS that ensures Esso’s assets are operated and maintained in keeping with corporate and industry safety standards. In this presentation we show how process safety is managed within OIMS and how the people within Esso individually and collectively contribute to it. Our work in this area has recently been captured in a training package that includes a DVD shown at the conference. It was created to raise the awareness and understanding of all Esso employees about the principles that underpin Esso’s approach to process safety. This abstract outlines how we approach process safety across the life-cycle of our facilities and the role people play in managing this very important aspect of our work. Our training reinforces the message that responsibility for effective management of process safety lies with every employee and how OIMS is designed to assist people to achieve the desired results where all risks are appropriately managed. We have sought to connect the concepts used to manage personal safety, which are well understood by the workforce, with those that are needed to understand how to manage process safety.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".