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Record W4406972323 · doi:10.1139/cjce-2024-0368

Hydraulic characteristics and energy dissipation of a new baffle-drop shaft with two wet sides

2025· article· en· W4406972323 on OpenAlexvenueno aff
Jun Chen, Pak-Yan Liang, Y. Lu, Teng Wu

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsnot available
Fundersnot available
KeywordsBaffleDissipationDrop (telecommunication)MechanicsEnvironmental scienceGeotechnical engineeringEngineeringMaterials scienceStructural engineeringMechanical engineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

To satisfy the operational capacity and structural safety requirements of larger flow rates under urban flood risk, a new baffle-drop shaft with two wet sides has been proposed. A turbulence mathematical model is established to simulate the hydraulic characteristics of traditional and new shafts, when the dimensionless flow rate ( Q * ) ranges from 0.1 to 0.25. Compared to that of the traditional shaft, the operational capacity of the new shaft is larger. The drainage of the new shaft is still smoother, but water blockage occurs in the traditional shaft. The pressure in the new shaft walls and baffles is smaller than that of the traditional shaft. The energy dissipation rate is higher than that of the traditional shaft. Consequently, this research could provide more reasonable design for dropshafts in deep tunnel drainage systems.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.971

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 head0.004
GPT teacher head0.181
Teacher spread0.177 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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