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Record W4401621362 · doi:10.2478/johh-2024-0014

Channel deformation around non-submerged spur dikes with different alignment angles under ice cover

2024· article· en· W4401621362 on OpenAlexafffund
Rahim Jafari, Jueyi Sui

Bibliographic record

VenueJournal of Hydrology and Hydromechanics · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversity of Northern British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSpurCover (algebra)GeologyDeformation (meteorology)Channel (broadcasting)DikeGeotechnical engineeringEngineeringPetrologyMechanical engineeringOceanographyTelecommunicationsPaleontology

Abstract

fetched live from OpenAlex

Abstract This study explores how the ice cover on water surfaces affects the deformation of the channel bed around non-submerged spur dikes. Laboratory experiments have been conducted by using two types of model ice cover with different roughness coefficients and three sands with different median grain sizes. The effects of various layout angles of non-submerged spur dikes on the maximum scour depth and scour patterns around spur dikes have been evaluated. Results showed that the dike orientation angle is the critical factor influencing the maximum scour depth. The presence of an ice cover and its roughness coefficient dramatically affect the channel bed deformation around spur dikes. The combined effect of the dike orientation angle, ice cover roughness, and flow Froude number resulted in different scour patterns. For instance, the upstream length of scour holes decreases by approximately 60% when the dike angle changes from 90º to 60º, while an increase in flow rate by about 50% leads to a 20% increase in the downstream length of scour holes. Equations have been derived to determine the maximum scour depth around spur dikes, considering the effects of ice cover, bed material and the dike layout angles.

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 score0.156
Threshold uncertainty score0.379

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 head0.006
GPT teacher head0.198
Teacher spread0.193 · 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

Citations12
Published2024
Admission routes2
Has abstractyes

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