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Record W2993124749 · doi:10.3138/chr.2019-0005

A History of Oil Spills on Long-Distance Pipelines in Canada

2019· article· en· W2993124749 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Historical Review · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsYork University
Fundersnot available
KeywordsPipeline transportOil spillEnvironmental sciencePipeline (software)Crude oilPetroleumEnvironmental protectionPetroleum engineeringEngineeringEnvironmental engineeringGeology

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.717
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.010
GPT teacher head0.185
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it