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Record W1893927404 · doi:10.1002/2014jc010416

A breaking internal wave in the surface ocean boundary layer

2015· article· en· W1893927404 on OpenAlexaffabout
Danielle Wain, Jonathan M. Lilly, Adrian H. Callaghan, Igor Yashayaev, Brian Ward

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsBedford Institute of Oceanography
FundersIrish Research CouncilEuropean Space AgencyNational Science FoundationScience Foundation IrelandEli Lilly and CompanyNational University of Ireland
KeywordsInternal waveBreaking waveSurface waveSurface layerGeologyTurbulenceBoundary layerInstabilityWind waveMixed layerGeophysicsPhysicsGeodesyMechanicsLayer (electronics)ClimatologyOpticsOceanographyWave propagationMaterials science

Abstract

fetched live from OpenAlex

Abstract High‐temporal resolution measurements in the Labrador Sea surface layer are presented using an upwardly profiling autonomous microstructure instrument, which captures an internal wave in the act of breaking at the base of the surface mixed layer, driving turbulence levels 2–3 orders of magnitude above the background. While lower‐frequency (near‐inertial) internal waves are known to be important sources of turbulence, we report here a higher‐frequency internal wave breaking near the ocean surface. Due to observational limitations, the exact nature of the instability cannot be conclusively identified, but the interaction of wave‐induced velocity with unresolved background shear appears to be the most likely candidate. These observations add a new process to the list of those currently being considered as potentially important for near‐surface mixing. The geographical distribution and global significance of such features are unknown, and underscore the need for more extensive small‐scale, rapid observations of the ocean surface layer.

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 score0.011
Threshold uncertainty score0.022

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 communication0.0010.000
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 head0.081
GPT teacher head0.324
Teacher spread0.243 · 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

Citations10
Published2015
Admission routes2
Has abstractyes

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