MétaCan
Menu
Back to cohort
Record W2149915245 · doi:10.1061/40655(2002)57

Evaluation of ADV Measurements in Bubbly Two-Phase Flows

2002· article· en· W2149915245 on OpenAlexaff
Minnan Liu, David Z. Zhu, N. Rajaratnam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFroude numberMechanicsTurbulenceObservational errorBoundary (topology)Boundary layerPitot tubeApproximation errorPrandtl numberPhysicsOpticsFlow (mathematics)MathematicsMathematical analysisConvectionStatistics

Abstract

fetched live from OpenAlex

Acoustic Doppler Velocimeters (ADV) have been widely used in measuring three dimensional turbulent velocities. A new development of ADV, MicroADV from SonTek, is used to measure the flow in free hydraulic jumps with Froude numbers of 2.0, 2.5 and 3.32. The effects of air bubbles and boundary on the measurements of MicroADV are evaluated. Measurements of the mean air concentration and mean velocity were obtained using a fiber optic probe and a Prandtl tube respectively. It was found that the error of mean velocity measured by MicroADV is increased linearly with the air concentration at very low concentration. The boundary effect on the measurements of MicroADV starts when the distance from the measurement point to the boundary is less than about 3 cm. The relative error of MicroADV measurements increases when the distance reduces, and at a distance of 0.5 cm the relative error is estimated to be 12 ∼ 25%. The relative error is within 6% for the situations without the effects of air bubble and boundary.

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)0.005
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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.085
GPT teacher head0.311
Teacher spread0.226 · 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

Citations33
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicHydrology and Sediment Transport ProcessesFrench-language works237,207