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Record W1988356121 · doi:10.1139/l09-051

Smoothed particle hydrodynamics hybrid model of ice-jam formation and release

2009· article· en· W1988356121 on OpenAlex
Simon Nolin, Varvara Roubtsova, Brian Morse, Tung Quach

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsHydro-QuébecUniversité Laval
FundersUniversity of Alberta
KeywordsSmoothed-particle hydrodynamicsBreakupGeologyMechanicsBarotropic fluidParticle (ecology)MeteorologyPhysics

Abstract

fetched live from OpenAlex

This article presents a numerical public domain model, SPIKI, to simulate water and ice dynamics during ice-jam formation and river breakup events. The model has two independent coupled components. The first is a one-dimensional (1-D) finite volume Saint-Venant hydrodynamic model, whereas the second is a two-dimensional (2-D) model called smoothed particle hydrodynamics (SPH) ice-rubble model that simulates ice dynamics. Application to an idealized test case demonstrated the effect of a variable angle of internal friction and the effect of ice-bank friction during the formation of an ice jam. Application to an actual event on the Saint John River in New Brunswick, Canada reproduced, within the certainty of the observed data, an observed ice-jam profile and the rise in water level and discharge after the release of the jam.

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.

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 score0.282
Threshold uncertainty score0.367

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 head0.179
Teacher spread0.172 · 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