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Record W3186616085 · doi:10.1029/2020jc016575

Observations of Multiple Internal Wave Packets in a Tidal River Plume

2021· article· en· W3186616085 on OpenAlexafffund
Sabine Rijnsburger, Raúl P. Flores, Julie D. Pietrzak, Kevin G. Lamb, Nicole L. Jones, Alexander R. Horner‐Devine, Alejandro J. Souza

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Waterloo
FundersNatural Environment Research CouncilStichting voor de Technische WetenschappenNatural Sciences and Engineering Research Council of Canada
KeywordsPlumeFront (military)Froude numberGeologyPanacheInternal waveHydrostatic equilibriumField (mathematics)Atmospheric sciencesOceanographyMechanicsMeteorologyPhysicsFlow (mathematics)

Abstract

fetched live from OpenAlex

Abstract Remotely sensed images document the occurrence of multiple packets of internal solitary waves (ISWs) in the Rhine River plume at the same time. We use a combination of field observations, and non‐hydrostatic and hydrostatic modeling to understand the processes that lead to the generation and retention of multiple ISW packets within the Rhine plume. Previous numerical modeling shows that the tidal plume front is trapped in the mid‐field plume for more than one tidal cycle due to tidal straining and recirculation within the plume, resulting in the presence of multiple fronts in the near‐and mid‐field plume regions. In this work, we show how variations in the strength of these fronts can lead to the release of ISW packets. We conclude that the retention of the fronts in the mid‐field region of the plume and modulation in the strength of the fronts can explain the presence of multiple ISW packets. A frontal Froude number analysis shows that fronts generated during the previous ebb tide can release ISWs in addition to the newly released tidal plume front.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.068
GPT teacher head0.296
Teacher spread0.228 · 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

Citations14
Published2021
Admission routes2
Has abstractyes

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