Ice-Seabed Gouging Database: Review and Analysis of Available Numerical Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it