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Record W2078786238 · doi:10.7901/2169-3358-2008-1-499

REGULAR AND BREAKING WAVES IN WAVE TANK FOR DISPERSION EFFECTIVENESS TESTING

2008· article· en· W2078786238 on OpenAlexaff
Erik Wickley-Olsen, Michel C. Boufadel, Tom King, Zhengkai Li, Ken Lee, Albert D. Venosa

Bibliographic record

VenueInternational Oil Spill Conference Proceedings · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsBreaking waveDissipationDispersion (optics)Wave tankWave heightMechanicsDeep waterWave shoalingWind wavePhysicsAcousticsWave propagationGeologyMechanical waveOpticsOceanographyLongitudinal waveThermodynamics

Abstract

fetched live from OpenAlex

ABSTRACT The wave tank (32 m long × 2.0 m high × 0.6 m wide) at the Bedford Institute of Oceanography in Nova Scotia was used to simulate the propagation and breaking of deep water waves using a flap-type wavemaker. The water profile and velocity were measured using a wave gauge and an Acoustic Doppler Velocimeter (ADV). The wave periods of interest ranged between 1.18 and 2.08 seconds. A technique for generating breaking waves at the same location in the tank was used to obtain a spilling and a plunging breaker. We evaluated the energy dissipation rate at various depths in the tank for regular and breaking waves. Plunging breaking waves had heights of 0.25m. For the breaking experiments, the energy dissipation rate decreased from around 1.0 10−2 watts/kg a few centimeters below the surface to less than 5.0 10−4 watt/kg 20 cm deep in the water column. The regular waves had, on the average, an energy dissipation rate of 5.0 10−6 watt/kg deep in the water column. This indicates that breaking plays an important role in the dispersion of oil at sea.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.231
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2008
Admission routes1
Has abstractyes

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