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Measurement of Bed Load Velocity using an Acoustic Doppler Current Profiler

2002· article· en· W2168118024 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Hydraulic Engineering · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAcoustic Doppler current profilerSampling (signal processing)Coefficient of variationBed loadObservational errorDoppler effectShear stressEnvironmental scienceCurrent (fluid)StatisticsGeologyGeodesyMathematicsSediment transportMechanicsGeomorphologySedimentEngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

A new technique has been developed to measure the apparent velocity of bed load (va) using an acoustic Doppler current profiler. The technique involves estimating the bias in bottom tracking due to a moving bottom. Mean va measured at sampling stations in the gravel-bed Fraser River correlated well (r2=0.93,n=9) with mean bed load transport rates measured using conventional samplers. Mean va was also correlated (r2=0.44,n=19) with boundary shear stress estimated by a log-law fit to the mean velocity profile. Estimates of va from individual 5 s ensemble averages were extremely variable: the coefficient of variation for a sampling station ranged from 1.0 to 6.4, and 25 min of sampling were required to achieve stable estimates of the mean and coefficient of variation (within 5% error). Variance was due to both real temporal variability of transport and measurement error. The mechanisms that produce this variability are discussed and preliminarily quantified.

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.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.032
GPT teacher head0.232
Teacher spread0.200 · 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