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Record W4303712250 · doi:10.3390/jmse10101421

Influence of Dynamic Woody Debris Jam on Single Bridge Pier Scour and Induced Hydraulic Head

2022· article· en· W4303712250 on OpenAlexafffund
Wenjun Zhang, Ioan Nistor, Colin D. Rennie, Husham Almansour

Bibliographic record

VenueJournal of Marine Science and Engineering · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersNational Research Council Canada
KeywordsDebrisPierGeologyHead (geology)Coarse woody debrisDebris flowLarge woody debrisGeotechnical engineeringLandslideEnvironmental scienceHydrology (agriculture)GeomorphologyRiparian zoneEngineeringEcology

Abstract

fetched live from OpenAlex

A woody debris jam around a bridge pier causes a change in flow structure and results in additional scour and an increase in the hydraulic head upstream of the pier, threatening its stability and safety. In the present paper, the spatio-temporal formation of a dynamic woody debris jam formed piece by piece of debris wood was used to investigate the influence of woody debris jams from a life-cycle perspective which included the processes of its formation, growth, failure, and rebirth. Several debris jams were formed in sequence during each experimental test. The results showed that the additional scour generated by the first woody debris jam compared with the scour depth without debris was a function of blockage ratio of the first debris jam, while the influence of the subsequent woody debris jams depended on their dimensions compared with the previous jam. When the subsequent debris jam’s dimensions were larger than the previous one, the scour further increased; otherwise, the scour remained constant and equal to the previous one. In addition, the debris-induced hydraulic head was analyzed and found to be correlated with the Froude number and the debris jam dimensions.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.211
Teacher spread0.204 · 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 designObservational
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

Citations9
Published2022
Admission routes2
Has abstractyes

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