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Record W1513508207 · doi:10.1002/esp.2131

Bridging the gap between turbulence and larger scales of flow motions in rivers

2011· article· en· W1513508207 on OpenAlexaff
G. A. Marquis, André G. Roy

Bibliographic record

VenueEarth Surface Processes and Landforms · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsTurbulenceFlow (mathematics)GeologyMechanicsSediment transportBridging (networking)Flow conditionsMeteorologyPhysicsHydrology (agriculture)SedimentEnvironmental scienceGeotechnical engineeringGeomorphologyComputer science

Abstract

fetched live from OpenAlex

ABSTRACT Although flow turbulence in rivers is of critical importance to earth scientists, ecologists and engineers, its relations with larger flow scales are not well understood, thus leaving a fundamental gap in our knowledge. From an analysis of a long time series of the streamwise and vertical flow velocity fluctuations measured in a gravel‐bed river, we show that the signature of the fundamental turbulent flow structures (e.g. ejections and sweeps) is embedded within increasingly larger flow scales in a self‐similar manner. The imbrication of turbulent structures into large flow pulsations of flow acceleration and deceleration covers more than two‐orders of magnitude from a few seconds to nearly 10 minutes. This property is explained by the clustering of turbulent events creating an emergent pattern at larger scales. The size of the larger flow pulsations scales with the spacing of the pools and riffles in the river. This implies a mutual adjustment between turbulence generation mechanisms and long pulsations of flow acceleration and deceleration controlled by the bed morphology. These results bridge a gap in our understanding of flows in rivers and offer a new perspective on the interactions between the turbulent flow with larger scales of flow motion that are critical for sediment transport, habitat selection and fish behaviour. Copyright © 2011 John Wiley & Sons, Ltd.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.283

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 head0.017
GPT teacher head0.205
Teacher spread0.188 · 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

Citations16
Published2011
Admission routes1
Has abstractyes

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