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Record W2003930819 · doi:10.1002/esp.1847

Suspended sediment transport and deposition over a dune: Río Paraná, Argentina

2009· article· en· W2003930819 on OpenAlexaff
Ray Kostaschuk, Dan H. Shugar, Jim Best, Daniel R. Parsons, Stuart N. Lane, Rich Hardy, Oscar Orfeo

Bibliographic record

VenueEarth Surface Processes and Landforms · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsSimon Fraser UniversityUniversity of Guelph
Fundersnot available
KeywordsTrough (economics)SedimentSediment transportGeologyCrestAcoustic Doppler current profilerDeposition (geology)GeomorphologyHydrology (agriculture)Flux (metallurgy)BedformCurrent (fluid)Geotechnical engineeringOceanography

Abstract

fetched live from OpenAlex

Abstract Field data from the Rio Paraná, Argentina, are used to examine patterns of suspended sediment transport over a sand dune. Measurements of three‐dimensional velocity are made with an acoustic Doppler current profiler whilst suspended sediment concentration and particle size have been quantified using a laser in situ sediment scattering transmissometer. Suspended sediment concentration and streamwise and vertical sediment flux are highest close to the bed, with an upward vertical flux over the stoss side of the dune and downward flux over the lee side. Suspended sediment concentrations are higher over the crest compared with the trough and suspended sediment is coarsest near the bed. About 17% of the suspended‐load transported over the crest is deposited in the lee side before it reaches the trough. Most of this deposited sand is coarser sediment that originates close to the bed over the crest, a result consistent with simulations based on the model of Mohrig and Smith (Water Resources Research 1996; 32: 3207–3217) for the excursion lengths of sediment dispersed in the lee side of a dune. Copyright © 2009 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 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 score0.305
Threshold uncertainty score0.606

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 communication0.0010.000
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 head0.005
GPT teacher head0.204
Teacher spread0.198 · 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

Citations58
Published2009
Admission routes1
Has abstractyes

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