Iceberg-seabed interaction evaluation in clay seabed using tree-based machine learning algorithms
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
In Arctic offshore regions, the oil and gas hydrocarbons are transferred to the onshore basins through the subsea pipelines. However, the operational integrity of the subsea pipeline may be at risk of collision with traveling icebergs, which gouge the seabed in the Arctic shallow waters. Even though these sea bottom-founded structures are buried at a secure depth below the seafloor, the pipeline is still threatened by the ice scouring event extended much deeper than the ice tip due to the shear resistance of the seabed soil. Modeling the sub-gouge soil characteristics is a challenging problem that requires costly experimental and long-running finite element (FE) simulations. To overcome these challenges, in this paper, the reaction forces and sub-gouge soil deformations in clay were modeled using an advanced extra tree regression (ETR) algorithm, as a quick and cost-effective alternative for the early design phases of pipeline engineering projects. Eight ETR models, ETR 1 to ETR 8, were developed by using the input parameters governing the iceberg-seabed interaction problem. The collected data were randomly split into 70% for training the machine learning (ML) models and 30% for testing purposes. The most accurate ETR models and the most significant input parameters were identified by performing a sensitivity analysis. The comparison of the most accurate ETR models and decision tree regression (DTR), random forest regression (RFR), and gradient boosting regression (GBR) algorithms proved that the ETR models had better performance to simulate the ice keel seabed interaction in clay.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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