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Record W1857485372 · doi:10.1111/cgf.12696

Reforming Shapes for Material‐aware Fabrication

2015· article· en· W1857485372 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 · 2015
Typearticle
Languageen
FieldEngineering
Topic3D Shape Modeling and Analysis
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsComputer scienceFabricationContext (archaeology)Geometric modelingComponent (thermodynamics)Object (grammar)Characterization (materials science)Relation (database)Variety (cybernetics)Topology (electrical circuits)Engineering drawingMechanical engineeringArtificial intelligenceNanotechnologyEngineeringMaterials scienceData mining

Abstract

fetched live from OpenAlex

Abstract As humans, we regularly associate shape of an object with its built material. In the context of geometric modeling, however, this inter‐relation between form and material is rarely explored. In this work, we propose a novel data‐driven reforming (i.e., reshaping) algorithm that adapts an input multi‐component model for a target fabrication material. The algorithm adapts both the part geometry and the inter‐part topology of the input shape to better align with material‐aware fabrication requirements. As output, we produce the reshaped model along with respective part dimensions and inter‐part junction specifications. We evaluate our algorithm on a range of man‐made models and demonstrate a variety of model reshaping examples focusing only on metal and wooden materials.

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 score0.963
Threshold uncertainty score0.452

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 head0.025
GPT teacher head0.232
Teacher spread0.206 · 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