United States Coast Guard Academy
Bibliographic record
Abstract
Icing threatens the stability of vessels that operate in high-latitude waters. The accumulation of ice on vessels depends on meteorological conditions and vessel design. Meteorological data is input into either the Overland or Modified Stallabrass algorithms which calculate the amount of icing that will occur. Ice coverage in the Arctic region has changed significantly since algorithms were developed in the 1980s. The goal of the National Weather Service (NWS) and the Meteorological Service of Canada (MSC) is to create a cohesive model that determines the icing rate throughout the North American region. A comprehensive overview of vessel icing algorithms that compared their strengths and weaknesses was conducted to lead to the eventual revision and development of a new algorithm. Specifically, ongoing efforts to obtain new observation data from vessels in the North American region will be used to recalibrate the algorithms by relating the model’s output to the severity of icing described by the observations. After relating the new observational data, a different Overland predictor value can be derived from the simplified heat balance of ice surface [1]. A constant value, for the liquid water content equation, \n = ∗ 10 ∗ ∗ exp(−), in the Modified Stallabrass algorithm can be calculated with updated data [2]. Current observational data applied to the algorithms will reflect the meteorological conditions in the Arctic. Progress made in the understanding of the meteorological conditions and associated augmentations necessary to the development of accurate icing predictions for vessel safety will be reported.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.502 | 0.303 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".