Bibliographic record
Abstract
Leaks and spills have been endemic on long-distance oil pipelines in Canada since the mid-twentieth century. Evidence from the National Energy Board (neb) pipeline incident reports reveals a track record of thousands of spills totalling millions of litres of oil across the country. What causes onshore oil spills? Why do they occur? Where have they occurred? What have been the environmental consequences of these incidents? This article explores the history of onshore oil spills on federally regulated long-distance pipelines since the mid-twentieth century. It argues that oil pipeline spills are an endemic characteristic of complex enviro-technical systems built primarily for economic efficiency rather than environmental protection. Based on the analysis of incident reports submitted to the neb, the article finds that, while frequent, onshore oil spills in Canada have been variable in scale and have had a wide range of potential adverse environmental effects, depending on location, product type, and volume. The causes of such spills have also been variable, conforming to no obvious pattern over time. Instead, oil pipeline spills have occurred most often in an unpredictable fashion, posing great challenges for policy development. These spills have also represented a proportionally small fraction of the total oil delivered on Canada’s long-distance pipelines, but, in absolute terms, this has meant the uncontrolled release of many millions of litres of oil into the environment.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.021 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".