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Record W1965341313 · doi:10.1068/b12984

Recursive Voronoi Diagrams

2003· article· en· W1965341313 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

VenueEnvironment and Planning B Planning and Design · 2003
Typearticle
Languageen
FieldEngineering
Topic3D Modeling in Geospatial Applications
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsVoronoi diagramDelaunay triangulationBowyer–Watson algorithmCentroidal Voronoi tessellationGenerator (circuit theory)Tessellation (computer graphics)Set (abstract data type)Computer scienceTheoretical computer scienceConstrained Delaunay triangulationAlgorithmMathematicsObject (grammar)Topology (electrical circuits)CombinatoricsGeometryComputer graphics (images)Artificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

This paper introduces procedures involving the recursive construction of Voronoi diagrams and Delaunay tessellations. In such constructions, Voronoi and Delaunay concepts are used to tessellate an object space with respect to a given set of generators and then the construction is repeated every time with a new generator set, which comprises members selected from the previous generator set plus features of the current tessellation. Such constructions are shown to provide an integrating conceptual framework for a number of disparate procedures, as well as extending the existing functionality of the basic Voronoi and Delaunay procedures to variable spatial resolutions. Further, because they are shown to be fractal in nature, it is suggested that this characteristic can be exploited in the development of new strategies for spatial modelling.

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: none
Teacher disagreement score0.836
Threshold uncertainty score0.662

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 head0.024
GPT teacher head0.209
Teacher spread0.184 · 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