Evaluation of Emergency Response Protocols for Crude Oil Transportation: Pipeline vs Rail
Bibliographic record
Abstract
This capstone project reviews and evaluates the emergency response protocols for crude oil transportation via pipeline and rail. The growth of the Canada’s oil sands and the use of hydraulic fracturing are providing access to what were previously thought to be uneconomic oil and gas deposits. This coupled with our growing use of crude oil is changing the energy landscape in North America. To accommodate this changing environment, increased transportation of crude oil is necessary. The increase in energy production and transport has had a parallel increase in public awareness of energy and dangerous goods transport. Canadian transportation systems operate within a highly regulated environment. However, no activity is without risk, crude oil spills occur and sometimes, major disasters have happened. To minimize the damages caused by accidental spills, we must employ emergency response protocols. This paper describes and compares the emergency response protocols of both pipeline and rail transport of crude oil. We review two large incidents for both modes of transportation of crude oil to determine if the emergency response protocols established forth by the governing bodies were adequate. Based on the comparison of the two protocols, we recommend enhancements for the two protocols and suggest further areas of research to advance current regulatory and emergency response frameworks.
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 | 0.098 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".