MétaCan
Menu
Back to cohort
Record W3189083225 · doi:10.2478/johh-2021-0014

Local scour around a bridge pier under ice-jammed flow condition – an experimental study

2021· article· en· W3189083225 on OpenAlexaff
Jun Wang, Zhixing Hou, Hongjian Sun, Jueyi Sui, Bryan Karney

Bibliographic record

VenueJournal of Hydrology and Hydromechanics · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of TorontoUniversity of Northern British Columbia
FundersNational Natural Science Foundation of China
KeywordsPierFroude numberGeologyGeotechnical engineeringBridge scourFlow (mathematics)Dimensionless quantityFlow velocityMechanicsEngineeringStructural engineering

Abstract

fetched live from OpenAlex

Abstract The appearance of an ice jam in a river crucially distorts local hydrodynamic conditions including water level, flow velocity, riverbed form and local scour processes. Laboratory experiments are used for the first time here to study ice-induced scour processes near a bridge pier. Results show that with an ice sheet cover the scour hole depth around a bridge is increased by about 10% compared to under equivalent open flow conditions. More dramatically, ice-jammed flows induce both greater scour depths and scour variability, with the maximum scour depth under an ice-jammed flow as much as 200% greater than under equivalent open flow conditions. Under an ice-jammed condition, both the maximum depth and length of scour holes around a bridge pier increase with the flow velocity while the maximum scour hole depth increases with ice-jam thickness. Also, quite naturally, the height of the resulting deposition dune downstream of a scour hole responds to flow velocity and ice jam thickness. Using the laboratory data under ice-jammed conditions, predictive relationships are derived between the flow’s Froude number and both the dimensionless maximum scour depth and the dimensionless maximum scour length.

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 categoriesInsufficient payload (model declined to judge)
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 score0.640
Threshold uncertainty score1.000

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)0.0010.000

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 head0.016
GPT teacher head0.254
Teacher spread0.237 · 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.

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

Citations13
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Hydrology and HydromechanicsSame topicArctic and Antarctic ice dynamicsFrench-language works237,207