{"id":"W2165269741","doi":"10.1016/j.jcp.2009.10.002","title":"Diffusion generated motion using signed distance functions","year":2009,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Banff International Research Station for Mathematical Innovation and Discovery; University of Michigan; Alfred P. Sloan Foundation; National Science Foundation","keywords":"Kernel (algebra); Signed distance function; Algorithm; Diffusion; Convolution (computer science); Dynamics (music); Computer science; Motion (physics); Mathematics; Function (biology); Artificial intelligence; Discrete mathematics; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001756873,0.0001080651,0.0001604587,0.0001469299,0.0001647279,0.0001648848,0.0003102649,0.00003400629,0.000002229101],"category_scores_gemma":[0.00001057239,0.0001005969,0.0001183123,0.0006886765,0.00001888064,0.0007022138,0.00003742755,0.0001197106,0.000001410087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005535593,"about_ca_system_score_gemma":0.00009706403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001046018,"about_ca_topic_score_gemma":1.045526e-7,"domain_scores_codex":[0.9988638,0.00006249397,0.000398893,0.000134258,0.0004309279,0.0001096341],"domain_scores_gemma":[0.9985505,0.00005324966,0.0004286573,0.0001281185,0.0007695156,0.00006996648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001435571,0.0004209916,0.0001962068,0.000006020498,0.00002826937,0.000009911409,0.0002349015,0.2501313,0.003190261,0.6843551,0.001059967,0.06035267],"study_design_scores_gemma":[0.0002186304,0.0001428673,0.002189296,0.00002460582,0.00000690128,0.00002581826,0.000002698974,0.7447125,0.0005895426,0.251821,0.0001766162,0.00008949417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0382522,0.00005404335,0.9610234,0.0002314679,0.0002756202,0.00005482958,0.000001645526,0.00006096033,0.00004578899],"genre_scores_gemma":[0.8988064,0.00000609316,0.1005795,0.0003060282,0.0002802529,2.844992e-7,0.000006887669,0.000004945618,0.000009638166],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8605542,"threshold_uncertainty_score":0.4102224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02505540385065206,"score_gpt":0.2942155381472625,"score_spread":0.2691601342966104,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}