Bibliographic record
Abstract
Human Factors play an important role in the reduction of pipeline incidents. There has been little guidance specific to the Pipeline Industry related to Human Factors management, with the exception of Control Room Management, in the recent past. The Human Factors Working Group, formed during the 2013 Banff Pipeline Workshop, partnered with CSA Group to fill an identified gap in the industry related to Pipeline Human Factors. The result was the creation of an Express Document, CSA EXP248 Pipeline Human Factors. CSA EXP248 (EXP248) considers Human Factors through the life cycle of a pipeline asset. The main goal of this document is to improve pipeline safety performance through management of risks associated with Human Factors. It provides guidance to pipeline operators on the need and means to integrate Human Factors in all aspects of the Pipeline Life Cycle and Management System, with philosophy considerations for integration of Human Factors into a “fit for service” pipeline system. A review of EXP248 will illustrate how this document provides information that is scalable to organizations based on size and complexity. This paper reviews the new CSA Group Express Document process and its application for the Pipeline Industry. This paper will discuss kkey aspects of EXP248 that operators could apply and practical tips on the application of this guidance to the pipeline operating community. Areas highlighted during this review include elements of a Human Factors program, consideration of physical, organizational and cognitive demands of a job or task, and integration of Human Factors into Management Systems. Finally, the paper will describe next steps to create a CSA Group consensus standard for Pipeline Human Factors, where EXP248 will serve as a seed document. Lessons learned during the use of the new Express Document will be highlighted and the use of this new process for other applications explored.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.099 | 0.080 |
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".