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Record W2169409610 · doi:10.1002/jgrc.20291

Shoaling internal solitary waves

2013· article· en· W2169409610 on OpenAlexaff
Bruce Sutherland, Kai J. Barrett, Gregory N. Ivey

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsShoaling and schoolingBreaking waveAmplitudeGeologyMechanicsWave shoalingWaves and shallow waterInternal waveMechanical waveGeometryPhysicsWave propagationLongitudinal waveOpticsMathematicsOceanography

Abstract

fetched live from OpenAlex

The evolution and breaking of internal solitary waves in a shallow upper layer as they approach a constant bottom slope is examined through laboratory experiments. The waves are launched in a two‐layer fluid through the standard lock‐release method. In most experiments, the wave amplitude is significantly larger than the depth of the shallow upper layer so that they are not well described by Korteweg‐de Vries theory. The dynamics of the shoaling waves are characterized by the Iribarren number, Ir, which measures the ratio of the topographic slope to the square root of the characteristic wave slope. This is used to classify breaking regimes as collapsing, plunging, surging, and nonbreaking for increasing values of Ir. For breaking waves, the maximum interface descent, , is predicted to depend upon the topographic slope, s , and the incident wave's amplitude and width, A sw and L sw , respectively, as . This prediction is corroborated by our experiments. Likewise, we apply simple heuristics to estimate the speed of interface descent, and we characterize the speed and range of the consequent upslope flow of the lower layer after breaking has occurred.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.030
GPT teacher head0.292
Teacher spread0.262 · 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

Citations84
Published2013
Admission routes1
Has abstractyes

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