Pipeline plough performance in sand waves. Part 1: model testing
Bibliographic record
Abstract
Offshore pipelines are often buried in the seabed by ploughing a trench, placing the pipe at the base, and then backfilling. The ploughing operation is critical in terms of cost and project time, with increased risk due to uncertain soil conditions or geohazards. One problem that can be encountered is the presence of sand waves or megaripples on the seabed surface. This may affect the progress of the plough, prevent the plough from generating a level trench or modify the size of the spoil heaps for backfilling. These aspects have been investigated by conducting a series of small-scale model tests in the laboratory. These have revealed information about the plough kinematics and the resulting trench conditions when ploughing in sand waves with different wavelengths and amplitudes. It is shown that it may be possible to plough through regions of sand waves and estimate likely plough performance by knowing the sand wavelength and amplitude relative to the plough size.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".