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Record W2069587662 · doi:10.1175/2010jpo4464.1

Observations of a Large-Amplitude Internal Wave Train and Its Reflection off a Steep Slope

2010· article· en· W2069587662 on OpenAlexafffund
Daniel Bourgault, David Charles Janes, Peter S. Galbraith

Bibliographic record

VenueJournal of Physical Oceanography · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsMemorial University of NewfoundlandFisheries and Oceans CanadaUniversité du Québec à Rimouski
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Foundation for Climate and Atmospheric Sciences
KeywordsAmplitudeReflection (computer programming)Internal waveGeologyBoundary (topology)Reflection coefficientField (mathematics)GeodesyPhysicsGeophysicsOpticsMathematicsMathematical analysisComputer science

Abstract

fetched live from OpenAlex

Abstract Remote and in situ field observations documenting the reflection of a normally incident, short, and large-amplitude internal wave train off a steep slope are presented and interpreted with the help of the Dubreil–Jacotin–Long theory. Of the seven remotely observed waves that composed the incoming wave train, five were observed to reflect. It is estimated that the incoming wave train carried Ei = (24 ± 4) × 104 J m−1 to the boundary. The reflection coefficient, defined as the ratio of reflected to incoming wave train energies, is estimated to be R = 0.5 ± 0.2. This is about 0.4 lower than parameterizations in the literature, which are based on reflections of single solitary waves, would suggest. It is also shown that the characteristics of the wave-boundary situation observed in the field are outside the parameter space examined in previous laboratory and numerical experiments on internal solitary wave reflectance. This casts doubts on extrapolating current laboratory-based knowledge to fjord-like systems and calls for more research on internal solitary wave reflectance.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.321

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 head0.023
GPT teacher head0.252
Teacher spread0.229 · 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
Published2010
Admission routes2
Has abstractyes

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