Estimating marine icing on offshore structures using RIGICE04
Bibliographic record
Abstract
A program for simulating ice accretion on an offshore structure due to spray generation from wave-structure impacts is presented in this paper. The program is an upgraded version of RIGICE that was first developed in 1987 and incorporates a number of improvements, specifically: a more accurate expression for the equilibrium freezing point of seawater, an empirical expression for sponginess of marine ice as a function of air temperature, a spray liquid water content versus height model that is matched with field data, and a new algorithm for estimating the frequency of significant spray events that generate spray clouds above 10 m high. Comparisons of ice accretion predictions are presented between the current version, RIGICE04 and a previous version, N_RIGICE. Generally, RIGICE04 predicts lower total ice accretion mass than N_RIGICE. RIGICE04 results are also compared with measured ice accretion duration on an offshore rig operating on the East coast of Canada. The current prediction is in good agreement with the measured duration; while the N_RIGICE prediction was more than twice the measured duration. RIGICE04 is more accurate than N_RIGICE although more comparisons with field data are required.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".