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Record W2316167396 · doi:10.1061/40655(2002)113

Particle Image Velocimetry Applied to a Deflected Wall Jet

2002· article· en· W2316167396 on OpenAlexafffund
P. R. Onyshko, Mark Loewen, N. Rajaratnam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlumeParticle image velocimetryBaffleJet (fluid)MechanicsVelocimetryFlow (mathematics)Materials scienceGeologyOpticsPhysicsEngineeringTurbulenceMechanical engineering

Abstract

fetched live from OpenAlex

This paper presents the results of an experimental study of a deflected wall jet using particle image velocimetry (PIV). The challenges inherent to successfully applying PIV to a flow with a wide velocity range and over a large area are discussed. Deflected wall jets are of interest at the outlets of dams and other hydraulic structures for their potential to reduce the risk of structural collapse due to bed erosion. A high-velocity wall jet can be deflected away from the bed and towards the water surface using a baffle installed on the bed. In this study, a plane wall jet was formed in a rectangular flume with a self-contained re-circulating water supply and deflected upwards by a baffle affixed across the width of the flume bed. The flow field was contained within a 60.0 cm square field of view centred 65.0 cm from the gate. This large area was subdivided into fourteen tesserae, each with 15.75 cm square fields of view. The resulting PIV data was subsequently assembled into a data mosaic covering the entire region of interest.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.212
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

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 designBench or experimental
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

Citations6
Published2002
Admission routes2
Has abstractyes

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