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Record W1970543114 · doi:10.1063/1.2214538

Internal wave generation from rough topography

2006· article· en· W1970543114 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhysics of Fluids · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Alberta
FundersCore Research for Evolutional Science and Technology
KeywordsPhysicsInternal waveBuoyancyAmplitudeTurbulenceWavelengthMechanicsExcitationGravity waveBoundary layerOpticsComputational physicsWave propagation

Abstract

fetched live from OpenAlex

Through laboratory experiments we examine internal wave generation above and in the lee of finite-amplitude periodic topography having various degrees of roughness. We show that internal waves are generated not only by flow over the hills but also by flow over “boundary-trapped” lee waves and by vigorous turbulence created in the lee of sharp-crested hills. For low values of the excitation frequency, linear theory well predicts the internal wave frequencies but significantly overestimates the wave amplitudes because it neglects processes associated with boundary layer separation. When the excitation frequency exceeds the buoyancy frequency, turbulence results in the excitation of internal waves with frequencies approximately 0.72±0.05 of the buoyancy frequency and vertical displacement amplitudes ranging between 1.5% and 2% of the horizontal wavelength.

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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.859

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 head0.014
GPT teacher head0.191
Teacher spread0.176 · 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