Global Mooring Loads for Semi-Submersible Station Keeping in Pack Ice
Bibliographic record
Abstract
Abstract Approaches are presented in this paper for estimating the global mooring loads and response of a semi-submersible drilling rig, as a result of pack ice loading. The focus is on loading events from pack ice conditions relevant to the Grand Banks, where the pack ice typically consists of small floes and limited concentrations. The current practice for semi-submersible drilling operations on the Grand Banks is to avoid contact with pack ice by disconnecting and moving off-station in the event of an ice incursion. From a global loads perspective this may be unnecessary, given that the typical pack ice is of low severity and mooring loads may well be within acceptable limits. To be able to operate in pack ice while moored, operators need to demonstrate that the moored semi-submersible will have sufficient structural and mooring capacity to withstand the ice loads. Some existing semi-submersible hulls have ice strengthening in place as specified by a classification society, with associated allowable operating criteria in terms of ice conditions. These operating criteria are to ensure sufficient structural capacity given the ice conditions. No standardized approaches are currently available to quantify global pack ice loads and associated offsets for moored semi-submersibles, which are needed to assess the required mooring capacity. The objective of this paper is to address this gap and present approaches that can assist in specifying allowable operating criteria for station-keeping in pack ice.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".