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Record W2134942330 · doi:10.1139/l09-088

Intensity and characteristic length of braided channel patternsThis paper is one of a selection of papers in this Special Issue in honour of Professor M. Selim Yalin (1925–2007).

2009· article· en· W2134942330 on OpenAlexafffundvenue
Peter Ashmore

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSinuosityIntensity (physics)Channel (broadcasting)Scale (ratio)Dimensionless quantityWavelengthFlow (mathematics)MechanicsBifurcationGeologyGeometryHydrology (agriculture)Computer scienceMathematicsGeotechnical engineeringPhysicsTelecommunicationsGeographyOpticsCartography

Abstract

fetched live from OpenAlex

Recent research on braided river morphology shows that the intensity (number of anabranches) of braiding channels increases with dimensionless discharge and (or) stream power. This variation in intensity reflects the adjustment of total sinuosity of the river to imposed gradient at a given discharge and grain size. Only a subset of channels is active at a given time and this active braiding intensity reflects the limited number of channels that can sustain bed load transport as the flow is divided. This is governed mechanistically by the dynamics of bifurcations and avulsions. Braided channel networks also have a characteristic length scale (or scales) related to the wavelength of the bars from which braiding develops and to the scale of the bars and confluence–bifurcation units within the braided network. The range of scales is limited by the size (and, therefore, number) of the active channels within the network and the width of the entire river.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.996

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)0.0050.000

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 head0.006
GPT teacher head0.182
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

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

Citations55
Published2009
Admission routes3
Has abstractyes

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