Iceberg Load Software Update Using 2019 Iceberg Profile Dataset
Bibliographic record
Abstract
Abstract The Iceberg Loads Software (ILS) was developed initially to determine design iceberg loads for the Hebron Gravity Base Structure (GBS). The ILS framework has since been adapted for assessing iceberg loads on other structures such as the West White Rose Platform, subsea protection structures, pipelines laid on the seabed and floating production structures (spars and FPSOs). When the ILS was developed, the available iceberg geometry dataset (collected in the 1980s) was relatively limited, which required certain assumptions (i.e., flat wall interaction) and parametrizations (i.e., length distribution, length/draft/mass relationships, eccentricity, etc.) in the formulation of the interaction model. Renewed iceberg profile collection began in 2012, with ongoing improvements in the data collection methodology such that, of the 200 iceberg profiles collected from 2012 onwards, 134 were collected in 2019. The profile data were collected using LiDAR for the iceberg sail and multibeam sonar for the keel. The ILS has been updated using the recent three dimensional (3D) profiles, and a comparison of original versus updated iceberg load distributions for a generic structure show a decrease in loads. Updated ILS loads are compared with another iceberg load analysis tool that directly incorporates iceberg profile data rather than relying on some of the assumptions and parametrizations used in the original ILS formulation. This comparison shows some differences, particularly for extreme loads, which are the subject of on-going investigation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.016 |
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".