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Record W2243896970

Field observation of seawater spray droplets impinging on the upper deck of an icebreaker

2013· article· en· W2243896970 on OpenAlexaboutno aff
Toshihiro Ozeki, Genki Sagawa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsSeawaterSea sprayIcingMeteorologyEnvironmental scienceSwellDeckOceanographyMarine engineeringGeologyAtmospheric sciencesGeographyEngineeringAerosol
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.205
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2013
Admission routes1
Has abstractyes

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