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Record W2966099225 · doi:10.1680/jencm.18.00034

Characteristics of two-dimensional counter-flowing wall jets

2019· article· en· W2966099225 on OpenAlexaff
A. S. Ramamurthy, Magda Tudor, Sang Soo Han, Diep Vo

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Engineering and Computational Mechanics · 2019
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsConcordia University
Fundersnot available
KeywordsDilutionJet (fluid)MechanicsMixing (physics)Penetration (warfare)Jet streamWakeChemistryPhysicsThermodynamicsMathematics

Abstract

fetched live from OpenAlex

Wastewater plant effluents discharged into streams are expected to undergo dilution in a short reach. Jets provide rapid mixing and enhance dilution of the effluent discharged into the stream. The counter-flowing wall jet expansion angle, the geometry of the dividing streamline and the jet penetration length were found using the measured mean axial jet velocity data. These jet characteristics were expressed in terms of the jet velocity ratio (jet exit velocity against main stream velocity). A salt solution of known concentration was injected into the jet flow. The distribution of the salt concentration was determined at several cross-sections of the jet wake using a concentration meter. The standard deviation of the salt concentration provided a measure of jet mixing. When standard deviation reached zero at a cross-section, mixing was considered to be complete.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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 designSimulation or modeling
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

Citations2
Published2019
Admission routes1
Has abstractyes

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