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Record W1882620406 · doi:10.1115/1.4031507

Measurement and Characterization of the Initial Geometric Imperfections in High Strength U-ing, O-ing and Expanding Manufactured Steel Pipes

2015· article· en· W1882620406 on OpenAlexaff
Muntaseer Kainat, J. J. Roger Cheng, M. Martens, Samer Adeeb

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsCanadian Natural ResourcesUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceHigh strength steelCharacterization (materials science)High resolutionScannerStructural engineeringComposite materialEngineeringGeologyNanotechnologyElectrical engineering

Abstract

fetched live from OpenAlex

The geometric imperfections in high strength U-ing, O-ing and expanding (UOE) manufactured pipes are investigated in this paper using a high-resolution 3D surface scanner, and a reverse engineering and inspection software. The geometric analyses show that the initial imperfection patterns in the UOE manufactured pipes are not at all random, although the magnitudes of imperfections may vary across specimens. These patterns of outside radii and pipe wall thickness imperfections consistently appear along the length of the specimens regardless of their D/t ratios and manufacturer. The sources of these imperfections can potentially be traced back to the UOE manufacturing process.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.018
GPT teacher head0.229
Teacher spread0.211 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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