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Record W2044281221 · doi:10.1029/2010jc006776

Propagation speeds of strongly nonlinear near-surface internal waves in the Strait of Georgia

2011· article· en· W2044281221 on OpenAlexafffundabout
Caixia Wang, Rich Pawlowicz

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsInternal waveKorteweg–de Vries equationOblique caseNonlinear systemWave packetAmplitudeGeologyWave propagationSurface wavePhysicsGeodesyAcousticsMeteorologyGeophysicsMechanicsOptics

Abstract

fetched live from OpenAlex

[1] A novel aerial observational method for studying internal features in the coastal ocean is developed and tested in a study of large nonlinear internal solitary-like waves. Photogrammetrically rectified oblique photo images from a circling aircraft are used to track a number of internal wave packets for periods of up to one hour in the Strait of Georgia, British Columbia, Canada. Combining these sequences with coincident water column data allows us to obtain a more complete view of the spatial structure of internal waves. Highly accurate measurements of wave propagation speeds and directions are possible. The applicability of various weakly nonlinear theories in modeling propagation of the observed large-amplitude waves is tested. The measured wave speeds enable us to differentiate between classic internal wave models. The linear, KdV (Korteweg-de Vries), and BO (Benjamin-Ono) models are applied with and without background shear. After background shear effects are included, it is found that a continuously stratified BO equation can predict propagation speeds within observational error, and that this is not true for other theories. The technique may be useful in future studies of oblique internal wave interactions.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.286
Teacher spread0.246 · 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 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

Citations37
Published2011
Admission routes3
Has abstractyes

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