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Record W3118971522 · doi:10.1139/cjce-2019-0536

Velocity Distribution and Attenuation Characteristic in Hydraulic Jumps on Rough Beds

2021· article· en· W3118971522 on OpenAlexvenueno aff
Lei Wang, Mingjun Diao

Post-publication record

NatureRetraction
ReasonDuplication of/in Article;
Date12/9/2021 0:00
Flagged by OpenAlex?Yes

Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsnot available
Fundersnot available
KeywordsAttenuationHydraulic jumpDistribution (mathematics)GeologyMechanicsGeotechnical engineeringHydrology (agriculture)MathematicsPhysicsFlow (mathematics)Mathematical analysis

Abstract

fetched live from OpenAlex

This study was conducted to investigate the velocity distribution and attenuation in free jumps on rough beds.Based on the length scale of jump length L j , the velocity distribution of the free jump on a rough bed can be divided into four parts by three typical sections where are in the position of x=0.4L j , x=0.8L j , and x=1.2L j .It seems that the velocity distribution near section x=0.4L j is the most uneven.The velocity attenuation rate in the bottom half of the water is larger than that in the top half of the water.The attenuation of the maximum velocity u m is mainly done from x=0 to x=0.8L j .The results show the mixed triangular corrugated floor increases the resistance of hydraulic jump development and is very efficient in energy dissipation.

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)0.001
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.177
Teacher spread0.172 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicHydraulic flow and structuresFrench-language works237,207