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Record W3036492617 · doi:10.1680/jenes.19.00037

Design of submerged vane matrices to accompany a river intake in Australia

2020· article· en· W3036492617 on OpenAlexvenueno aff
Ronald William Lake, Saeed Shaeri, Lalantha Senevirathna

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
Fundersnot available
KeywordsSedimentHydrology (agriculture)CloggingSediment transportContext (archaeology)HydraulicsTurbidityEnvironmental scienceRiver morphologyGradationGeotechnical engineeringGeologyFlow (mathematics)Sediment controlInfiltration (HVAC)Stream restorationFlow conditionsGeomorphologyEngineeringGeographySTREAMSMathematicsGeometry

Abstract

fetched live from OpenAlex

This paper focuses on the design of river-based submerged vane matrices in a regional Australian context. The specific study site is proximal to intake screens within the riverbed that experiences continual clogging from sediment deposition. The local authority experiences discontinuous supply to the water-treatment plant owing to a high sediment concentration. Data on the river flow, depth, turbidity and rainfall were collected, and analyses of sediment composition, gradation and density were carried out within this research. Through the study of incipient sediment motion and hydraulics, with specific reference to the method of Shields, the shear stress and critical velocity necessary for sediment motion were determined. The results provided guidance for an iterative modelling design of submerged vanes within the local context ultimately configured as symmetrical diverging vane matrices. Placed a short distance upstream of the existing intake screens at a 30° angle to the river flow direction on two symmetric arms, the three vane arrays generate secondary flow as a function of the existing flow around the vanes. The purpose of the vane matrix is twofold, firstly to redirect sediment away from the existing infiltration galleries and secondly to scour the riverbed within the bounds of the matrices. With a subsequent increase in the depth of water, intake screen(s) placed within and orthogonal to the flow would benefit from an increased depth in combination with a reduction in sediment delivery to the screen(s).

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 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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.015
GPT teacher head0.209
Teacher spread0.194 · 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 teacher head, 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

Citations11
Published2020
Admission routes1
Has abstractyes

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