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Record W2091567668 · doi:10.1115/ipc2014-33632

Risk Management for Lateral Channel Movement at Pipeline Water Crossings

2014· article· en· W2091567668 on OpenAlexaboutno aff
Jan Bracic, Craig D. Malcovish, Eugene K. Yaremko

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsPipeline transportIntegrity managementGeohazardChannel (broadcasting)ErosionPipeline (software)GeologyHydrology (agriculture)Environmental scienceEngineeringGeotechnical engineeringGeomorphologyLandslideTelecommunications

Abstract

fetched live from OpenAlex

Pembina Pipeline Corporation (Pembina) owns and operates close to 10,000 km of crude, natural gas liquids (NGL), and natural gas pipelines across North America, with the majority of assets in western Alberta and eastern British Columbia. The Pembina pipeline network includes over 1,600 river and stream crossings, most of which are subject to varying degrees of vertical and/or lateral erosion. 1,260 crossings were in Alberta at the onset of the study. Identifying potential lateral erosion hazards is a critical component of geohazard management program for pipeline integrity. In 2012, Pembina initiated a three-phase program to proactively address lateral-stability issues at river and stream crossings in Alberta: phase one identified and short-listed crossings that have potential lateral channel-shifting problems; phase two assessed which short-listed crossings have insufficient cover depth to accommodate the potential channel-shifting activities and ranked these crossings as high risk of exposure; and phase three will develop plans for repair and/or replacement of the high-risk crossings. Through this program, Pembina explores the significance of lateral erosion and encroachment at pipeline water crossings of various vintages, with regards to pipeline integrity. This paper provides discussion as to how crossing geohazard risks are identified, with particular emphasis on stream bank erosion, and how this fits into Pembina’s overall risk management program. As well, selected case studies are provided.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.185
Teacher spread0.180 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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