Bibliographic record
Abstract
Rising oil and natural gas production in North America is outpacing the transportation capacity of our pipeline infrastructure. As one of us (Green) discussed in a previous study in this series, The Canadian Oil Transport Conundrum, Canada is poised to dramatically increase production of bitumen from oil sand deposits in Western Canada. In the face of expanding production and pipeline bottlenecks, more oil is moving by rail in both Canada and the United States, but transport of oil by rail (or other non-pipeline transportation modes) carries its own set of risks. While pipelines may leak, trains and trucks can crash, hurting individuals, as we saw in Lac-Megantic in July 2013, and barges can sink. There is no perfectly risk-free way to transport oil, or anything else for that matter.Although North America is home to 825,000 kilometers of pipeline in Canada and 4.2 million kilometers in the US, US government authorities still insist on blocking additional pipeline construction.After reviewing available data on the safety of different oil-transport modes, we conclude that the evidence is clear: transporting oil by pipeline is safe and environmentally friendly. Furthermore, pipeline transportation is safer than transportation by road, rail, or barge, as measured by incidents, injuries, and fatalities.For North America to realize the massive economic benefits from the development of those oil sands, the transport conundrum must be solved. At present, resistance to pipeline transport is sending oil to market by modes of transport that pose higher risks of spills and personal injuries such as rail and road transport.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".