Combining safety approaches to bring hazards into focus: An oil sands tailings case study
Bibliographic record
Abstract
Abstract At least 50 hazardous occurrences associated with tailings facilities occurred in the Canadian mining industry between 2000‐2014. Further investigation revealed a dearth of information on worker safety around tailings storage and transport facilities. Workers at oil sands tailings operations are exposed to hazardous scenarios, including loss of containment and line of fire. These are the similar scenarios that manifest in traditional process industries, with the notable differences between traditional process industries and tailings operations being the frequency of incidents, pressures, volumes, and temperatures. The presence of hazardous scenarios and lack of incident reviews illustrate the need for increased attention to be paid to worker safety at oil sand tailings operations as well as enhancements to current hazard identification tools. Process safety management tools such as bow tie diagrams can be applied to tailings operations to visually identify unwanted events (process and occupational health and safety related), potential threats, consequences, and controls used to prevent incidents from occurring. They also serve as a tool for continuous improvement and show any over‐reliance on one type of control, such as administrative controls or personal protective equipment. This research combines safety approaches using the bow tie analysis of seven hazardous operational activities in the oil sands tailings operations as a case study. The impact of behavioural safety on the controls is also analyzed. This research facilitated the sharing of tailings safety best practices among oil sands operators and regional contractors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".