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Record W2135369192 · doi:10.1109/gmap.2004.1290038

Feature based retargeting of parameterized geometry

2004· article· en· W2135369192 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWorkflowComputer scienceRetargetingGridFeature (linguistics)Mesh generationParameterized complexityParametric statisticsReuseSurface (topology)Parametric surfaceTemplateSolid modelingGeometryAlgorithmArtificial intelligenceMathematicsEngineeringFinite element methodDatabase

Abstract

fetched live from OpenAlex

This paper presents an approach for mapping layouts of parametric surface patches to a target 3D geometry. Its main contribution is to facilitate the feature based placement of an arbitrary network of patches, assuring that both boundaries and parametric flow conform to features of the target shape. The technique, referred to as dynamic templates, describes the algorithms and interface of a reverse engineering system, Paraform, that integrates techniques relying on a judicious choice of automation and user guided tools. Our approach is based on a use of constrained optimization for the fairing of structured surface grids, where grid points can be unconstrained in 3D or constrained to lie within the parameter space of curves, surfaces, or other geometry. We present our results as case studies in large industrial workflow problems, involving the reuse of geometric data.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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