Global and Local Iceberg Loads for an Arctic Floater
Bibliographic record
Abstract
Abstract Analysis of loads from icebergs for an Arctic Floater has been performed. The determination of iceberg loads was guided by the ISO 19906 International Standard. Global and local loads on the hull, as well as mooring loads, were studied. To determine loads at the specified levels of exceedance, probabilistic methodology using Monte Carlo methods was used, taking into account the areal density of the ice features (for example the number of icebergs per 10,000 km2), and the probability distributions of the size and mass of the features, their added mass, their velocity, eccentricity of the collision, compliance of the structure, and the strength of the ice. The influence of surrounding sea ice on iceberg loads, as well as iceberg management including detection, towing and disconnection were analysed. Iceberg areal density was determined based on an analysis of available data, including information on the various forms, such as tabular or bergy bit. The strength of ice in collisions involving icebergs was modeled based on full scale crushing data from ship rams with multi-year ice. The pressure-area scale effects associated with ice-structure interaction were taken into account, considering also scatter in pressure measurements for a given contact area. Separate relationships for local and global loads were used. The analysis accounts for the Ekman current acting on an iceberg, together with wind and wave drift forces. Motions of the floating vessel and the icebergs in sea states were analyzed, accounting for the prevailing environmental conditions. Probabilities of collision along the length of the floater and in the vertical plane have been calculated using Monte Carlo methods. A variety of assumptions have been made: no ice management, management with and without disconnection, and the effect of sea ice on detectability and management is included.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 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".