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Record W2099068810 · doi:10.3934/nhm.2011.6.401

An adaptive finite-volume method for a model of two-phase pedestrian flow

2011· article· en· W2099068810 on OpenAlex

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.

Bibliographic record

VenueNetworks and Heterogeneous Media · 2011
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsMount Allison University
FundersDeutsche Forschungsgemeinschaft
KeywordsFinite volume methodDegenerate energy levelsFlow (mathematics)Mathematical analysisPhase spaceSpace (punctuation)DiffusionStability (learning theory)MathematicsCountercurrent exchangeDimension (graph theory)Applied mathematicsMechanicsPhysicsGeometryComputer science

Abstract

fetched live from OpenAlex

A flow composed of two populations of pedestrians moving in different directions is modeled by a two-dimen\-sional system ofconvection-diffusion equations. An efficient simulation of the two-dimensional model is obtained by a finite-volume scheme combinedwith a fully adaptive multiresolution strategy. Numerical tests showthe flow behavior in various settings of initial and boundaryconditions, where different species move in countercurrent orperpendicular directions.The equations are characterized ashyperbolic-elliptic degenerate, with an elliptic region in the phase space, which in one space dimension is known to produce oscillation waves.When the initial data are chosen inside the elliptic region,a spatial segregation of the populations leads to pattern formation.The entries of the diffusion-matrix determine the stability of the model and the shape of the patterns.

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: Methods · Consensus signal: none
Teacher disagreement score0.789
Threshold uncertainty score0.599

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 head0.045
GPT teacher head0.286
Teacher spread0.240 · 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