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Direct Solutions for Uniform Flow Parameters of Wide Rectangular and Triangular Sections

2021· article· en· W3160483102 on OpenAlexaff
Ahmed A. Lamri, Said M. Easa, Mohamed Tewfik Bouziane, M. Bijankhan, Yan-Cheng Han

Bibliographic record

VenueJournal of Irrigation and Drainage Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputationHead (geology)Hydraulic headOpen-channel flowFlow (mathematics)GeometryChannel (broadcasting)Series (stratigraphy)Section (typography)Power seriesPotential flowMathematicsMechanicsSurface (topology)Mathematical analysisComputer scienceAlgorithmGeologyTelecommunicationsPhysicsGeotechnical engineering

Abstract

fetched live from OpenAlex

One of the general problems encountered in the design of open channels is the computation of normal depth, head loss, and discharge. A wide rectangular section is commonly used in natural streams and surface/sheet flow in watersheds and the triangular section is commonly used for irrigation and roadside channels. The normal depth or head loss is traditionally solved using a trial (iterative) procedure. This paper develops two direct solutions for the head loss and normal depth for the wide rectangular and triangular open channel sections. The explicit equations for the normal depths are developed in terms of fast converging power series. The maximum errors of the proposed explicit formulas are 0.65% and 2% for triangular and wide rectangular channels, respectively, compared with 5% and 8% for the existing methods.

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)0.003
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.009
GPT teacher head0.197
Teacher spread0.189 · 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
GenreMethods

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

Citations3
Published2021
Admission routes1
Has abstractyes

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