Development of an Iceberg Impact Load Assessment Tool
Bibliographic record
Abstract
Abstract Icebergs can pose risks to platforms in arctic and subarctic regions. These risks require careful consideration during design, and as well during operations. Platforms must be designed to withstand potential impacts from icebergs, or to disconnect and move offsite to avoid impacts. ISO 19906 allows use of ice management to mitigate iceberg and sea-ice actions. In the case of icebergs, management may include detection, monitoring, towing, disconnection and evacuation. Threat assessment is also a critical input to the iceberg management decision-making process. For example, given one or more detected icebergs and available information on the iceberg and environment characteristics, what is the probability of exceeding platform design ice actions? Based on the threat assessment, better decisions can be made regarding which iceberg to manage, whether more information should be acquired, and whether shut-down or evacuation is needed. This paper describes a new tool developed to estimate the distribution of iceberg impact actions from an encroaching iceberg given concurrent metocean conditions, conditional on impact. The tool can be used in a number of ways depending on the information available to the user. It can be used to assess the threat from a single iceberg or can be used to compare actions from multiple icebergs in the region, or for the same iceberg but with changing weather conditions. The iceberg load assessment tool is demonstrated for several example cases on the Grand Banks, showing the benefit of improved iceberg characterization obtained through rapid iceberg profiling.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".