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Record W2143678957 · doi:10.1002/fld.848

Multidimensional positive definite advection transport algorithm (MPDATA): an edge‐based unstructured‐data formulation

2005· article· en· W2143678957 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

VenueInternational Journal for Numerical Methods in Fluids · 2005
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsRoyal Military College of Canada
FundersU.S. Department of Energy
KeywordsCurvilinear coordinatesPolygon meshFinite volume methodCartesian coordinate systemAdvectionUpwind schemeAlgorithmMesh generationComputer scienceUnstructured gridApplied mathematicsGeometryMathematicsMathematical analysisFinite element methodGridPhysicsMechanicsDiscretization

Abstract

fetched live from OpenAlex

Abstract We report a new development in the area of non‐oscillatory transport methods. We derive, discuss, and test the iterative upwind scheme MPDATA in the Finite Volume framework with the edge‐based data structure and arbitrary hybrid mesh. MPDATA has proven successful in simulations of geophysical flows using single block, structured cuboidal meshes, while employing continuous invertible mappings to accommodate time‐dependent curvilinear domains. Our motivation for the finite‐volume formulation and the choice of unstructured meshes is to facilitate the use of MPDATA schemes for a wider range of applications involving complex geometries and/or inhomogeneous anisotropic flows, where mesh adaptivity is advantageous. Our development preserves the signature benefits of the standard Cartesian‐mesh MPDATA scheme, i.e. the second‐order accuracy, sign‐preservation, and a full multidimensionality free of directional‐splitting errors. Copyright © 2005 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.454
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0010.000
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 head0.029
GPT teacher head0.376
Teacher spread0.347 · 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