MétaCan
Menu
Back to cohort
Record W2169605450 · doi:10.1109/cgi.2001.934686

Hierarchical implicit surface refinement

2002· article· en· W2169605450 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 Calgary
Fundersnot available
KeywordsConstraint (computer-aided design)HierarchyComputer scienceSurface (topology)Representation (politics)Object (grammar)Theoretical computer scienceDeformation (meteorology)AlgorithmGeometryArtificial intelligenceMathematicsMaterials science

Abstract

fetched live from OpenAlex

A novel method of hierarchical implicit modeling is presented in which an implicit object is modeled using a hierarchy of implicit surfaces. The hierarchy provides both layered local refinement and global deformation. Local refinement allows the introduction of higher-level detailed surfaces. Global deformation changes the overall shape of the surface while maintaining the integrity of surface details. The model is gradually refined by introducing appropriate new primitives in specified surface areas. Refinement constraints, such as a local area constraint and a level constraint, are designed to be applied to the implicit object so as to achieve finer control of the local surface. The method provides a dynamic representation of implicit surfaces and can be used in modeling complex implicit objects, animating the surfaces, and simulating the deformations of various objects.

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 score0.940
Threshold uncertainty score0.271

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 science0.0010.000
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 head0.030
GPT teacher head0.284
Teacher spread0.255 · 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