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Record W4283080264 · doi:10.1080/00221686.2022.2041497

Effects of developing ice covers on bridge pier scour

2022· article· en· W4283080264 on OpenAlexafffund
Dario A. B. Sirianni, Christopher Valela, Colin D. Rennie, Ioan Nistor, Husham Almansour

Bibliographic record

VenueJournal of Hydraulic Research · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPierBridge scourGeologyGeotechnical engineeringBridge (graph theory)Hydrology (agriculture)GeomorphologyEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Current research investigating ice-covered bridge pier scour has focused on the effects of fully developed ice jams. However, based on observations at various stages of development, ice covers are not always fully developed. This experiment investigated pier scour at various stages of a simulated floating ice jam’s development, with a constant flow rate. Various channel-spanning ice cover lengths were tested, along with one circular localized cover, and resulting pier scour was compared to free-surface flow pier scour. Five lengths of downstream initiated ice covers were tested, with lengths upstream of the pier being 0, 0.63, 1.33, 2.66 and 5.32 m. The local cover’s diameter was 0.27 m, three times the pier diameter. The 2.66-m ice cover produced the greatest scour and near-bed Reynold stress, with greater scour depth and volume, respectively, by 46.9% and 238% compared to free-surface flow conditions, whereas the local cover produced the smallest scour increase.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.327
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2022
Admission routes2
Has abstractyes

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