{"id":"W2947464908","doi":"10.1111/cgf.14020","title":"Learning Generative Models of 3D Structures","year":2020,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"China National Funds for Distinguished Young Scientists; National Natural Science Foundation of China","keywords":"Computer science; Generative grammar; Computer graphics; Generative model; Artificial intelligence; Probabilistic logic; Graphics; Generative Design; Rendering (computer graphics); Human–computer interaction; Computer graphics (images)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008066795,0.0007091981,0.0007669966,0.001157979,0.0003736162,0.001418804,0.001432449,0.001346671,0.003676011],"category_scores_gemma":[0.003265128,0.00100532,0.001790791,0.001068419,0.00146693,0.001595487,0.001500204,0.001757615,0.0008615137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001131548,"about_ca_system_score_gemma":0.0005801893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00465471,"about_ca_topic_score_gemma":0.008614102,"domain_scores_codex":[0.9994603,0.0001732157,0.00001756698,0.000145949,0.000157183,0.00004590355],"domain_scores_gemma":[0.9984446,0.00107616,0.00009913246,0.0002035962,0.0001199842,0.00005653997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002041009,0.00002144496,0.000826491,0.00004305481,0.00003673335,0.00005483567,0.00008197958,0.9191169,0.001193397,0.04723226,0.001425004,0.02994744],"study_design_scores_gemma":[0.000002848177,0.00000406544,0.00005583914,0.000006059503,0.00000302867,0.00001187439,0.000005039008,0.9840619,0.0001808938,0.01515369,0.0005115662,0.000003249338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01500868,0.0003086368,0.9812878,0.0002554833,0.00002846264,0.00002375221,0.0001982656,0.0006145941,0.002274452],"genre_scores_gemma":[0.7289806,0.001373767,0.2557686,0.0004432376,0.0001476352,0.0002811036,0.001896333,0.0005679724,0.0105408],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00465471,"threshold_uncertainty_score":0.01229745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01917956591427749,"score_gpt":0.2057867027624657,"score_spread":0.1866071368481882,"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."}}