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Record W2883121136 · doi:10.1145/3203207

Energized Rigid Body Fracture

2018· article· en· W2883121136 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

VenueProceedings of the ACM on Computer Graphics and Interactive Techniques · 2018
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsÉcole de Technologie Supérieure
FundersNational Science Foundation
KeywordsAnimationFracture mechanicsDeformation (meteorology)Computer scienceRigid bodyDisplacement (psychology)Continuum mechanicsFracture (geology)Computer animationComputer graphicsRigid body dynamicsMechanicsPotential energyKinetic energyElastic energyClassical mechanicsStructural engineeringComputer graphics (images)PhysicsEngineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

Compelling animation of fracture is a vital challenge for computer graphics. Methods based on continuum mechanics are physically accurate, but computationally expensive since they require computing elastic deformation. In many applications, this elastic deformation is imperceptible, so simulation methods based on rigid body dynamic with breakable constraints are popular in practice. Simply deleting constraints when thresholds on force or displacement are reached ignores the elastic energy that is stored just before fracture, which is captured by continuum mechanics based methods. Our approach computes the energy stored in these constraints when they are broken, and reintroduces it to the system as kinetic energy. As a result, our method is able to animate energetic fracture scenarios with results comparable to continuum mechanics approaches, but with the computational efficiency of rigid body simulation.

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: Empirical · Consensus signal: none
Teacher disagreement score0.815
Threshold uncertainty score0.869

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 communication0.0000.001
Open science0.0030.003
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 head0.012
GPT teacher head0.284
Teacher spread0.272 · 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