Bibliographic record
Abstract
ABSTRACT Vessel icing resulting from sea spray is a significant risk to maritime operations in the high latitudes. Despite the acknowledged risk that it imposes, there is limited climatological information that can be used for assessment and mitigation purposes. Here we use a parameterization of the icing rate that has been validated against observed cases of vessel icing and the Interim Reanalysis from theECMWF(ERA‐I) to develop the first climatology of the vessel icing for the wintertime subpolar North Atlantic. Three regions, the Labrador Sea, the Iceland Sea and the Greenland Sea, are examined in further detail. In all three regions, the icing rate and the frequency of occurrence of vessel icing increases towards the ice edge. The Labrador Sea is shown to have the highest risk of icing followed by the Greenland Sea and then the Iceland Sea. In particular, icing rates in excess of 4 cm h−1are predicted to occur approximately 35%, 20% and 10% of the time during the winter in the Labrador, Greenland and Iceland Seas, respectively. On the monthly mean time scale, the severity of icing in all three regions is determined, to lowest order, by the location and depth of the Icelandic Low and its subsidiary feature, the Lofoten Low. In addition, heavy icing events in all three regions are shown to be associated with the passage of synoptic scale low‐pressure systems that trigger cold air outbreaks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".