MétaCan
Menu
Back to cohort
Record W2116800226 · doi:10.1115/1.4031812

Development of Probability of Ignition Model for Ruptures of Onshore Natural Gas Transmission Pipelines

2015· article· en· W2116800226 on OpenAlexaff
Chio Lam, Wenxing Zhou

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsWestern University
Fundersnot available
KeywordsPipeline transportNatural gasConfidence intervalIgnition systemPipeline (software)Petroleum engineeringEnvironmental scienceTransmission (telecommunications)StatisticsInterval (graph theory)Hazardous wasteEngineeringWaste managementMathematicsEnvironmental engineeringMechanical engineering

Abstract

fetched live from OpenAlex

A log-logistic probability of ignition (POI) model for ruptures of onshore natural gas transmission pipelines is proposed in this paper. The parameters of the proposed POI model are evaluated based on a total of 188 rupture incidents that occurred on onshore gas transmission pipelines in the U.S. between 2002 and 2014 as recorded in the pipeline incident database administered by the Pipeline and Hazardous Material Safety Administration (PHMSA) of the U.S. Department of Transportation. The product of the pipe internal pressure at the time of rupture and outside diameter squared is observed to be strongly correlated with POI and therefore adopted as the sole predictor in the POI model. The maximum likelihood method is employed to evaluate the model parameters. The 95% confidence interval and upper confidence bound on the POI model are also evaluated. The model is validated against an independent set of rupture incident data reported in the literature. The proposed POI model will facilitate the quantitative risk assessment of onshore natural gas transmission pipelines in the U.S.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.455
Threshold uncertainty score0.306

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 head0.034
GPT teacher head0.267
Teacher spread0.233 · 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 designBench or experimental
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

Citations4
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Pressure Vessel TechnologySame topicCombustion and Detonation ProcessesFrench-language works237,207