Ice Gouge Risk to Offshore Pipelines – Making the Most of Available Data
Bibliographic record
Abstract
Marine pipelines, and sub-sea oil and gas production facilities are potentially at risk from gouging ice features. Risk depends on structural configuration, structural resistance, the physical dimensions of the ice gouges (i.e.,width, depth, length and orientation) and the frequency with which these occur. Characteristic expressions for calculating risk of ice damage to buried pipelines are provided in the paper, with some discussion on the consequences. While many of these are straightforward, they are not presently in the ice engineering literature. The scope includes iceberg and pressure ridge keel gouge processes. The authors have been involved in studies of ice gouge risk over the last few years for the Grand Banks of Newfoundland, the Okhotsk Sea off Sakhalin Island and Lake Erie in Canada. In each case, ice gouge processes posed unique challenges. Strategies are outlined for dealing with the absence of comprehensive data on ice gouge presence and dimensions due to the limitations of site-specific seabed surveys.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".