A Zero Accident Strategy for Oil Pipelines: Enhancing HSE Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it