Bibliographic record
Abstract
Societal risk has been investigated in the United Kingdom by the Health and Safety Executive (HSE) (2), the Netherlands (1), and most recently Canada (3). All methodologies focus on the high consequences of a significant event with a low probability of occurrence. Enbridge Pipelines Inc. uses various techniques to assess risk of mainline pipe and facilities. An index based risk model has been used for both mainline and facility risk assessment to provide relative risk values. These models have proven to be a useful means of risk evaluation. However, there are certain facilities or segments of pipe that have been identified by operating personnel as sensitive areas for reasons other than those defined for high consequence areas under 49CFR195 for liquid operations and 49CFR192 for gas operations regulated by the United States Department of Transportation. This paper proposes a method of identifying and quantifying these higher sensitive areas that could be applied to the any organization in the Oil and Gas Industry by incorporating societal risk into existing risk methodologies. For the purposes of this paper, societal risk is defined as the presence of a sensitive area from a social viewpoint with the potential for enhanced risk control or negative public reaction in the event of a significant incident at a specified location. Societal risk is approached in this paper as a multiplier to the total risk score obtained from existing risk assessment techniques. This multiplier can be applied to risk models, quantitative risk evaluations or other numerical based risk methodologies. This paper discusses the development of a societal risk factor, including a definition and scope for societal risk, and application of this risk multiplier to existing risk assessment techniques. Risk management strategies that may result from the use of a societal risk factor are also included.
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 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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".