Field observation of seawater spray droplets impinging on the upper deck of an icebreaker
Bibliographic record
Abstract
Seawater spray icing is a major problem faced not only by fishing vessels and trawlers but also by commercial vessels. Marine disasters caused by ice accretion occur frequently in cold regions. However, even today, deicing continues to be a manual operation that usually involves the use of a hammer. To address icing on the ship, sea spray generation, spray delivery, and heat transfer for ice accretion are important. In this study, the authors developed a seawater droplet counter for measuring the droplets impinging on ships. The field observation was made on the upper deck of the CCGS Louis S. St-Laurent during the voyage to the Northwest Passage and the Canada Basin. Because the observation period was late July and August of 2012, the purpose of this observation was to obtain the relationship between pitching and rolling of the ship and seawater spray generation. The weather conditions, acceleration of the ship, and size and number of seawater droplets were measured. The marine condition and spray generation were recorded by using a monitoring system set up on the upper deck of the ship. Droplet size distributions were obtained in rough weather. Preliminary result shows that long-period swells did not contribute to the increase in the amount of seawater spray, although the pitching angle of the ship increased with swell. The seawater droplet counter recorded large numbers of particles during a storm. The time series of the droplet counter suggested that the particles were caused by raindrops, because droplets were detected continuously.
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 | 0.000 |
| 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".