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Record W4248043334 · doi:10.1111/cgf.12045

Stroke Parameterization

2013· article· en· W4248043334 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

VenueComputer Graphics Forum · 2013
Typearticle
Languageen
FieldEngineering
Topic3D Shape Modeling and Analysis
Canadian institutionsAutodesk (Canada)
Fundersnot available
KeywordsComputer scienceComputer graphicsSurface (topology)Ideal (ethics)Computer graphics (images)Texture mappingGraphicsPlanarPlane (geometry)Exponential functionComputer visionArtificial intelligenceAlgorithmGeometryMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Abstract We present a novel algorithm for generating a planar parameterization of the region surrounding a curve embedded in a 3D surface, which we call a stroke parameterization. The technique, which extends the well‐known Discrete Exponential Map [ SGW06 ], uses the same basic geometric transformations and hence is both efficient and easy‐to‐implement. We also handle self‐intersecting curves, for which a 1–1 map between the original surface and the plane is not possible. Stroke parameterizations provide an ideal coordinate space for solving a variety of computer graphics problems. We present applications including tiling texture and displacement along 3D brush strokes, procedural texturing along 3D paths, and user‐guided crease extraction.

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 score0.812
Threshold uncertainty score0.359

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 head0.007
GPT teacher head0.178
Teacher spread0.171 · 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