Enhancing critical control management using bowties for high consequence risks at Rio Tinto
Bibliographic record
Abstract
Abstract In industries where high‐consequence events can result in severe impacts, effective risk management is essential. This paper presents Rio Tinto's evolving approach to managing such risks across its mining and minerals processing operations. The destruction of the Juukan Gorge rock shelters in 2020—a site of profound cultural significance to the Puutu Kunti Kurrama and Pinikura peoples—served as a pivotal moment for the company, emphasizing the value of adopting a more holistic approach to hazard identification and control. In response, Rio Tinto launched a comprehensive risk management uplift program that extends beyond traditional major hazards—such as process safety and tailings—to include cultural heritage and environmental considerations. Central to this program is the expansion of bowtie‐based critical control management, focusing on enhancing first‐line capability within a three‐lines‐of‐defense framework. The approach integrates best practices from the Energy Institute (EI), the Center for Chemical Process Safety (CCPS), and the International Council on Mining and Metals (ICMM). The paper explores the importance of clearly defining controls and critical controls, the role of centrally defined performance specifications, and best practices in bowtie methodology. It also includes a case study demonstrating the application of this approach to a major process safety hazard.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".