Ice-Seabed Gouging Database: Review and Analysis of Available Numerical Models
Bibliographic record
Abstract
Abstract Ice gouging or scour may damage structures buried in seabeds, hence research on this subject is important. Numerical modelling is one of the most flexible and least costly methods of studying ice gouging. Previously, information on existing numerical models and their results was scattered in the literature. A new database has been created that tabulates this information. The database can be used to search numerical results and analyze knowledge gaps and correlations that might exist, in order to better understand and further advance knowledge of ice gouging phenomena. The database contains information on 206 runs from 18 major numerical studies. Using the database, knowledge gaps have been assessed. A list has been made of topics which were given little attention despite their probable importance, including deformable ice keel, different-from-seabed trench backfill soil, and simultaneous interaction of pore water and soil matrix in cohesionless seabeds. The available numerical model results show that pre-set gouge depth and the maximum depth of subgouge soil deformation are nearly linearly correlated. Maximum pipeline strain as a function of test set-up parameters is assessed. Profiles of subgouge soil deformation with depth from various sources are also combined and compared in this paper.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".