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Record W4411174907 · doi:10.3390/w17121751

Enhancing the Unit Stream Power Dissipation Equation for Unlined Spillways: Incorporating Geometrical Parameters and Surface Irregularities

2025· article· en· W4411174907 on OpenAlexafffund
Yavar Jalili Kashtiban, Ali Saeidi, Marie‐Isabelle Farinas, Javier Patarroyo

Bibliographic record

VenueWater · 2025
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsUniversité LavalHydro-QuébecUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of CanadaHydro-Québec
KeywordsDissipationStream powerSurface (topology)Power (physics)Environmental scienceMechanicsMarine engineeringEngineeringGeotechnical engineeringMathematicsGeometryGeologyPhysicsErosionThermodynamicsGeomorphology

Abstract

fetched live from OpenAlex

The unit stream power dissipation (USPD) equation is widely used to predict water flow characteristics over spillways. However, the current formulation of the USPD equation may not provide accurate predictions of the energy dissipation rates of water flowing over unlined spillways, particularly when geometrical parameters and surface irregularities are considered. To address this issue, we modify the USPD equation to improve its accuracy. We determine how geometrical parameters and surface irregularities affect the accuracy of the USPD when applied to unlined spillways. Our modifications to the USPD equation account for these factors and improve predictions of the energy dissipation rate of water flowing over unlined spillways. We demonstrate that incorporating geometrical parameters and surface irregularities into the USPD equation improves the accuracy of estimated energy dissipation rates. Improved prediction accuracy has important implications for spillway design and maintenance, favoring safer and more effective water management systems. Our study highlights the need to consider geometrical parameters and surface irregularities when estimating USPD in unlined spillways.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.232

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 head0.014
GPT teacher head0.229
Teacher spread0.215 · 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 routes2
Has abstractyes

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