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Record W1945653003 · doi:10.1029/2004wr003777

Hydraulic geometry of secondary channels of lower Fraser River, British Columbia, from acoustic Doppler profiling

2005· article· en· W1945653003 on OpenAlexaffabout
Erica R. Ellis, Michael Church

Bibliographic record

VenueWater Resources Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHydraulicsGeologyAcoustic Doppler current profilerGeometryChannel (broadcasting)ScalingHydrology (agriculture)Doppler effectGeomorphologyGeotechnical engineeringPhysicsEngineeringOceanographyCurrent (fluid)TelecommunicationsMathematics

Abstract

fetched live from OpenAlex

The hydraulics and morphology of secondary channels within the lower Fraser River gravel reach were examined using data collected with an acoustic Doppler profiler during the large 2002 freshet. Data were collected over an area of channel (“subreach”), for a range of subreach morphologies (“upstream,” “mid,” and “downstream”). As suggested by visual evidence, at‐a‐station hydraulic geometry of subreach types stratified along gradients of width, depth, and velocity. Fish habitat is more abundant and more persistently available in the wide, deep downstream subreaches, but higher velocities preferred by some species occur in the mid stream and upstream subreaches. Additional high‐flow data, used to develop bank‐full scaling relations (classical “downstream” hydraulic geometry), conformed well to a simple power law up to and including data points from the main channel. However, width and depth exponents deviated from classical results. Investigation suggests that the relations observed in this study approach expected relations for constant slope and channel boundary materials.

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)0.000
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 score0.092
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.253
Teacher spread0.236 · 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

Citations42
Published2005
Admission routes2
Has abstractyes

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