Keeping the memory alive, preventing memory loss that contributes to process safety events
Bibliographic record
Abstract
Recurring process safety events (PSEs) are a real concern to the energy industry. Contributing causes to these events are quite often very similar. It appears as though the learnings from past events are not retained in the memories of the workforce, setting the stage for accidents to repeat. Even with best practices available to prevent such recurring accidents, these events continue to happen again and again. It seems as if something is missing, in order to effectively use the knowledge gained from so many past disasters and near misses, to prevent further PSEs. It was desired to develop a tool to aid ConocoPhillips Canada (CPC) in preventing memory loss that is contributing to PSEs. Some of the world's worst process safety accidents were reviewed to gather common learnings, and investigation reports of CPC past PSEs were analyzed to determine how prevalent the issue of memory loss is within the company. Best practices to prevent such memory loss were researched and found to be readily available, and yet for some reason, memory loss issues are very widespread. The cognitive sciences were looked to for an answer on how memories are developed and effectively retained. The field of education was researched, to determine how leading educators effectively teach learning to achieve high levels of memory retention. Through this the taxonomy table, a tool that has been used by educators to enhance teaching and learning for many years, was discovered. Then, effective safety communication methods that target memory retention were explored. All researched information was finally tied together, into a learning curriculum, consisting of various activities. These activities were constructed to advance the learning process toward an objective that had been carefully developed using the taxonomy table guidelines. This objective was “for the workers to integrate past process safety learnings to prevent future process safety events.” © 2013 American Institute of Chemical Engineers Process Saf Prog 33: 115–123, 2014
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".