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Record W2088373835 · doi:10.1115/ipc2002-27176

Natural Hazard and Risk Management for Pipelines

2002· article· en· W2088373835 on OpenAlexaffabout
K. Wayne Savigny, Michael J. Porter, Joyce Chen, Eugene K. Yaremko, Michael Reed, G. Urquhart

Bibliographic record

Venue4th International Pipeline Conference, Parts A and B · 2002
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsBGC Engineering (Canada)
Fundersnot available
KeywordsHazardPipeline (software)LandslidePipeline transportNatural hazardRisk managementHazard analysisRisk analysis (engineering)Identification (biology)Risk assessmentEngineeringNatural (archaeology)Civil engineeringEnvironmental resource managementEnvironmental planningEnvironmental scienceForensic engineeringComputer scienceGeographyBusinessEnvironmental engineeringReliability engineeringGeotechnical engineeringComputer security

Abstract

fetched live from OpenAlex

Pipeline systems must contend with many hazards, of which ground movements such as landslides and washouts represent one type. Under the broader umbrella term, natural hazards, individual ground movement threats can be subdivided into geotechnical and hydrotechnical hazards. A four-phase natural hazard and risk management system (NHRM) is being developed. Although research and development are ongoing, implementation over the past seven years spans approximately 25,000 km of main-line pipeline in North and South America. It complies with CSA requirements for ‘hazard identification’ as well as current standard-of-care guidelines related to case-law in Canada. It is designed as a simple yet reproducible methodology that can be operated by pipeline companies, particularly their field staff. The first two phases of hazard identification/assessment are described here with reference to a recent study of hydrotechnical hazards along the Trans Mountain Pipe Line Co. Ltd. main line from Hinton, Alberta to Kamloops, British Columbia in the mountains of western Canada. The relative hazard ratings generated by the Phase I and II methodology can be integrated into existing risk management methodologies used in the industry. Alternatively, the risk assessment and risk management methodology of the NHRM system can be used as outlined in this paper.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.764
Threshold uncertainty score0.503

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)0.0000.000

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 head0.014
GPT teacher head0.221
Teacher spread0.207 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations6
Published2002
Admission routes2
Has abstractyes

Explore more

Same venue4th International Pipeline Conference, Parts A and BSame topicGeotechnical Engineering and Underground StructuresFrench-language works237,207