MétaCan
Menu
Back to cohort
Record W4385873401 · doi:10.55274/r0012068

PR-393-205100-R01 IRIS X-Ray CT Qualification for Flexible Pipe Inspection (Phase 1)

2021· report· en· W4385873401 on OpenAlexaff
Agnes Decroux, Kassem Kalo, Keith Swinden

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsCybernet Systems Corporation (Canada)
Fundersnot available
KeywordsSubseaArmourEngineeringAcousticsMaterials scienceLayer (electronics)Marine engineeringComposite material

Abstract

fetched live from OpenAlex

There are several techniques available to inspect single wall carbon steel pipelines including; Magnetic flux leakage (MFL), ultrasonic testing (UT), Electro-Magnetic Acoustic Transducer (EMAT), Phased Array, guide wave testing (GWT), etc. However, for more complex structures such as flexible pipelines the technology available to inspect them is far more limited. PRCI commissioned a program (SPIM 2-1) under the Subsea TC (2017-2020) to evaluate all known and suspected technologies that could be used to provide a detailed subsea inspection of a flexible riser. PRCI produced four samples of flexible pipe containing pre-manufactured cracks and corrosion defects which were located in; the outer armour layer, inner armour layer, pressure vault and carcass. The samples were used for blind testing of all identified inspection technologies. On conclusion of the SPIM 2-1 program, HR-XCT was identified as the technology showing the most promise and a follow-on program (SPIM 2-2) was commissioned to further explore the capabilities. This report will show the way in which high resolution image clarity and image manipulation was extracted from the HR-XCT system when used on the PRCI flexible pipe samples. The XCT results from SPIM 2-2 will be presented to show the initial setup of the experiment and 2D and 3D high resolution sectioned images from the testing. These images clearly identify and characterize 100% of the pre-manufactured defects introduced into the samples in all layers.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.874
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.316
Teacher spread0.266 · 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.

Study designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicOffshore Engineering and TechnologiesFrench-language works237,207