Comparative Analysis Of The Safety Of Transporting Crude Oil In Canada
Bibliographic record
Abstract
This study was conducted to identify the safest mode of transporting crude oil by analysing and comparing safety records of rail, pipeline and tanker to their respective GHG emissions and cost benefits. The need to assess the safety of transporting crude oil became necessary after a train carrying 7.7 million litres of crude oil derailed, and exploded at Lac- Mègantic, Quèbec in 2013. The explosion killed 47 people and released 6 million litres of oil into the environment. A study conducted by Furchtgott-Roth & Green (2013) revealed that pipeline is safer and cheaper to transport oil based on safety records and not in regards to environmental and economic impacts. The safety data used in that study was from 2005 – 2009, and some major incidents have occurred afterwards, which makes it necessary for another research to be conducted. In this study, pipeline has the most acceptable safety records from the overall basis when assessing the safety records of the modes of transporting crude oil, using three criteria: safety, GHG emission and cost benefits. However, when appraised separately based on each criterion, tanker was considered to have the lowest safety records based on accident rate, distance traveled, volume of oil shipped and release of products. In terms of economics, pipeline emerged the cheapest, while rail was recommended as the safest in terms of GHG emissions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".