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Record W2151085636 · doi:10.1029/2011jc007308

Reduced upper ocean turbulence and changes to bubble size distributions during large downward heat flux events

2011· article· en· W2151085636 on OpenAlexafffund
Svein Vagle, Johannes Gemmrich, Helen Czerski

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of VictoriaFisheries and Oceans Canada
FundersDivision of Ocean SciencesOffice of Naval ResearchFisheries and Oceans CanadaNatural Environment Research CouncilSight Research UK
KeywordsTurbulenceHeat fluxBubbleFlux (metallurgy)DissipationAtmospheric sciencesPhysicsMechanicsHeat transferThermodynamicsMaterials science

Abstract

fetched live from OpenAlex

During the Radiance in a Dynamic Ocean (RaDyO) field study south of Hawaii in September 2009, simultaneous observations of total heat flux, upper ocean turbulence, and bubble size distributions suggest that large downward heat flux modulates the upper ocean turbulence dissipation rates and subsequently the upper ocean bubble field. The observations show that the turbulence dissipation rates near the ocean surface are reduced by a factor of 10 during periods with high downward heat flux (>400 W m−2). Simultaneously, the observations of bubble size distributions at a depth of 0.5 m show that there were significantly lower concentrations of bubbles with radii >100 μm than during a winter study in the Gulf of Mexico. Also, the number of bubbles with radii >200 μm is found to be dependent on the heat flux, with fewer such bubbles during stable (positive heat flux) conditions. The reduced number of larger bubbles reduces the effect of the bubble field on optical reflectance by up to a factor of 3 compared to other locations at similar wind speeds.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.027
GPT teacher head0.275
Teacher spread0.248 · 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 designObservational
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

Citations33
Published2011
Admission routes2
Has abstractyes

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