A Zero Accident Strategy for Oil Pipelines: Enhancing HSE Performance
Bibliographic record
Abstract
Abstract This work provides options to reduce the number of oil pipeline adverse events caused by human actions, poor performance of facilities, accidents, emergencies, and external events. These alternatives provide useful tools to decision makers to prevent such events and improve the security, integrity and resilience of existing oil and gas transportation infrastructure. This research implements an in-house visualization software that is based on Structured Query Language (SQL), Geographic Information Systems (GIS), and publicly available data. The system stores, sorts, and processes strategic geo-referenced data: pipelines infrastructure, transported volumes, sociodemographic factors, land use, illegal pipeline taps, and area impacted by oil pipelines incidents. By identifying the main factors that could impact the pipeline infrastructure, the system generates several graphical representations to assist in risk analysis. The work also analyses and proposes improved pipeline monitoring systems, emergency responses protocols, and non-technical tools to address operational and safety challenges for oil pipelines near local communities. The results provide valuable information for the formulation of policy and regulations to enhance pipeline safety. This work develops a comprehensive strategy based on data analysis, monitoring systems, emergency response protocols and non-technical tools to assist decision makers to improve operational safety and prevent events that could cause serious damage to local communities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".