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Record W2105049066 · doi:10.1109/cgi.2000.852320

Modeling Murex cabritii sea shell with a structured implicit surface modeler

2002· article· en· W2105049066 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
KeywordsComputer sciencePython (programming language)Computer graphics (images)Programming languageSubdivision surfaceWorkflowTexture mappingShell (structure)Image warpingConstructive solid geometryInterface (matter)Rendering (computer graphics)Engineering drawingPolygon meshArtificial intelligenceDatabaseEngineeringParallel computing

Abstract

fetched live from OpenAlex

Implicit surface modeling systems have been used since the mid-1980's for the generation of cartoon like characters. Recently implicit models combined with constructive solid geometry (CSG) have been used to build engineering models with automatic blending. This work is built on a structured implicit modeling system which includes CSG, warping, 2D texture mapping and operations based on the BlobTree, and its application to the generation of a complex and visually accurate biological model of the sea shell Murex cabritii. Since the model is purely procedurally defined and does not rely on polygon mesh operations, it is resolution independent and can be rendered directly using ray tracing. An interface has been built to the BlobTree using an interpreted programming language (Python). The language interface readily allows a user to procedurally describe the shell based on numeric data taken from the actual object.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score0.616

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 science0.0010.000
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 head0.025
GPT teacher head0.251
Teacher spread0.226 · 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