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Record W1982771859 · doi:10.1139/t09-091

Pipeline plough performance in sand waves. Part 2: kinematic calculation method

2010· article· en· W1982771859 on OpenAlexvenueno aff
M. F. Bransby, Michael Brown, Keith Lauder, Andrew Hatherley

Bibliographic record

VenueCanadian Geotechnical Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research Council
KeywordsPloughTrenchGeotechnical engineeringSubmarine pipelineSeabedGeologyKinematicsEngineeringPipeline (software)Marine engineeringMechanical engineeringMaterials science

Abstract

fetched live from OpenAlex

Offshore pipelines can be buried in the seabed by ploughing a trench, placing the pipe at its base, and then backfilling. The presence of sand waves or megaripples on the seabed surface can affect the progress of the plough and prevent the plough from generating a level trench with a uniform trench depth. A calculation method has been presented that makes assumptions about the motion of the plough to predict the kinematics of ploughs through regions of nonuniform seabeds. Results from the calculation methodology are compared with those from small-scale model tests with good agreement, and the detailed kinematics of ploughs are then examined. The calculation method suggests that as a plough moves through a sand-wave field, the oscillation of the plough about the skids results in the trench base being formed alternately by the share tip and heel. The new method allows prediction of likely offshore plough performance given known plough geometry, sand wavelength, and wave amplitude and may be used as a tool for assessing the feasibility of pipeline ploughing in zones of sand waves or megaripples.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.213
Teacher spread0.205 · 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 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

Citations3
Published2010
Admission routes1
Has abstractyes

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