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Record W2560145238 · doi:10.1115/ipc2016-64085

Updated Estimates of Frequencies of Pipeline Failures Caused by Geohazards

2016· article· en· W2560145238 on OpenAlexaffabout
Michael J. Porter, Gerald R. Ferris, Mark Leir, Miguel Leach, Mario Haderspock

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsBGC Engineering (Canada)
Fundersnot available
KeywordsGeohazardPipeline transportPipeline (software)TerrainGeologyIntegrity managementTraverseRouting (electronic design automation)PiggingEnvironmental scienceMining engineeringComputer scienceSeismologyGeographyCartographyLandslideGeodesy

Abstract

fetched live from OpenAlex

This paper provides an updated compilation of geohazard-related pipeline failure frequencies for onshore hydrocarbon gathering and transmission pipelines, with a particular emphasis on the analysis of data from Western Europe, Western Canada, the US, and South America. The results will be of interest to owners, operators, regulators and insurers who wish to calibrate estimates of geohazard failure frequency and risk on planned and operating pipelines, particularly for pipelines traversing mountainous terrain. It concludes with an estimate of the global annual frequency of failures caused by geohazards on hydrocarbon gathering and transmission pipelines, and postulates that this failure frequency should continue to decline when measured on a per kilometer basis due to ongoing improvements in geohazard recognition, routing and design of new pipelines, and improvements to integrity management practices for operating pipelines.

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)0.001
metaresearch head score (Gemma)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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

Citations13
Published2016
Admission routes2
Has abstractyes

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