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Record W2166001796 · doi:10.2112/08-1090.1

Reynolds Stress Estimates in a Tidal Channel from Phase-Wrapped ADV Data

2010· article· en· W2166001796 on OpenAlexaffabout
Colin D. Rennie, Alex E. Hay

Bibliographic record

VenueJournal of Coastal Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsDalhousie UniversityUniversity of Ottawa
Fundersnot available
KeywordsReynolds stressGeologyReynolds numberAcoustic Doppler velocimetryTurbulenceChannel (broadcasting)GeodesyMechanicsPhysicsLaser Doppler velocimetryTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Reynolds stress in a tidal channel was measured for a period of 11 days in July and August 2003. A 50 to 100 cm/s ebb tide jet issues from Corkum Channel into Lunenburg Bay on the Atlantic coast of Nova Scotia, Canada. An instrumented bottom pod was deployed in Corkum Channel on a sandy area in approximately 6 m mean water depth. We focus on measurements from two acoustic Doppler velocimeters: a SonTek ADVO and a Nortek Vector. The Vector displayed smooth transitions in 1-hour mean velocity and Reynolds stress. As expected, Reynolds stress magnitude was greatest during ebb tide. The ADVO velocity data were filtered to remove intermittent noise, apparently due to unexpected ambiguity velocity phase wraps. The unwrapping procedure improved estimates of mean streamwise velocity and Reynolds stress and reduced noise in the ADVO time series. The potential for bias in Reynolds stress estimates due to differential noise between acoustic Doppler velocimeter beams is considered.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.079
GPT teacher head0.398
Teacher spread0.319 · 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

Citations15
Published2010
Admission routes2
Has abstractyes

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