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Record W4288084341 · doi:10.1061/9780784484272.024

Application of SPF Large Displacement Type in Vancouver, Canada

2022· article· en· W4288084341 on OpenAlexaboutno aff
H. Nakazono, Norio Hasegawa, Kentaro Taniguchi, Toshio Imai

Bibliographic record

VenuePipelines 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsPipeline transportDisplacement (psychology)Settlement (finance)Offset (computer science)SubsidenceGeologyGeotechnical engineeringStructural engineeringEngineeringComputer scienceMechanical engineeringGeomorphology

Abstract

fetched live from OpenAlex

Water pipelines are vulnerable for failure at locations where they cross active fault lines. Steel pipe for crossing faults (hereinafter SPF) was developed by the authors as a seismic countermeasure construction method for pipelines which cross faults and has an extensive record of use in Japan in key trunk pipelines in the waterworks field. Subsequently, using the SPF technology, “SPF large displacement type” was developed, which could deform by low reaction force. With this improved SPF, the range of the SPF application is being expanded to include large ground displacements other than fault offset, such as subsidence and liquefaction settlement. This paper introduces the outline of the SPF large displacement type to expand the range of SPF application and its application through a case study in Vancouver, Canada. In the study, the application of SPF large displacement type was verified, using a numerical analysis.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.038
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.003
GPT teacher head0.185
Teacher spread0.182 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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