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Record W4409502537 · doi:10.5006/c2022-17785

Horizontal Directional Drilling in External Pipeline Coating Integrity

2022· article· en· W4409502537 on OpenAlexaboutno aff
Eric Pintueles

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Failure Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsDirectional drillingPipeline (software)Petroleum engineeringDrillingPipeline transportGeologyComputer scienceMarine engineeringMechanical engineeringEngineeringOperating system

Abstract

fetched live from OpenAlex

Abstract External coating testing for pipeline directional drilling installations or informally “Bore Test”. It is a very much requested test in Alberta, Canada. The conditions of the installation are severe for the integrity of the external coating. So, a procedure has been developed after the involvement in several projects. This presents the synergies between field data and cathodic protection theories. Two different field tests are presented by using DC and AC power sources furthermore a theoretical calculation but with given field data. Also, it is introduced a specific theory how to get the resistance of the soil. As the results, pipe-to-earth resistance was similar for both field tests and consistent with the theoretical calculation given field resistivity data. Any Horizontal Directional Drilling external pipeline coating integrity evaluation has its own limitations. Such as, site conditions, backfill settle, aboveground survey techniques access, as well as suitable equipment. Bore tests are run in different conditions therefore it is important to understand the engineering theories for these field tests.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.233
Teacher spread0.212 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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