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Comparing Five Kinematic Wave Schemes for Open-Channel Routing for Wide-Tooth-Comb-Wave Hydrographs

2021· article· en· W3126410816 on OpenAlexaffabout
Charles Luo

Bibliographic record

VenueJournal of Hydrologic Engineering · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsRoyal Jubilee Hospital
Fundersnot available
KeywordsHydrographKinematic waveRouting (electronic design automation)Channel (broadcasting)GeologyKinematicsOpen-channel flowFlow routingHydrology (agriculture)Computer scienceMeteorologyComputer networkSurface runoffGeotechnical engineeringDrainage basinGeographyPhysicsCartographyTurbulence

Abstract

fetched live from OpenAlex

Due to its simplicity, the kinematic wave is commonly applied to open-channel routing in watershed hydrologic modeling. However, these applications must fulfill certain conditions, such as relatively large riverbed slopes and long time of rise in the flow. This study attempts to determine the most appropriate kinematic wave scheme for open-channel routing in the highly regulated Peace River, Canada. Five schemes were used to simulate a 5-day hydrograph with sudden plunges and hikes. The outputs from these five schemes were compared visually and statistically with the observed hydrograph. It was found that all five schemes are applicable to open-channel routing for the highly regulated Peace River if the temporal and spatial increments are set properly. However, one scheme, which is a total variation diminishing (TVD) high-resolution scheme, is the most appropriate scheme for this purpose. This scheme allows large temporal and spatial increments while relatively high accuracy can be achieved.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.221
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.043
GPT teacher head0.238
Teacher spread0.195 · 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
Published2021
Admission routes2
Has abstractyes

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