{"id":"W4400818419","doi":"10.1145/3658155","title":"Split-and-Fit: Learning B-Reps via Structure-Aware Voronoi Partitioning","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Voronoi diagram; Computer science; Centroidal Voronoi tessellation; Artificial intelligence; Theoretical computer science; Computer graphics (images); Mathematics; Geometry","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":[0.00105495,0.001224959,0.001666915,0.001765413,0.0005700402,0.001311559,0.003684098,0.002394414,0.002459862],"category_scores_gemma":[0.00458411,0.001132649,0.001612222,0.001444285,0.001168145,0.002705036,0.00276959,0.001952286,0.001421027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009208127,"about_ca_system_score_gemma":0.001025152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007104224,"about_ca_topic_score_gemma":0.007817068,"domain_scores_codex":[0.9991688,0.0001373211,0.0000336785,0.000278095,0.0002976365,0.00008451383],"domain_scores_gemma":[0.9986876,0.0005526361,0.0001432961,0.0002912763,0.0002419352,0.00008329183],"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":[0.0002276926,0.0001084427,0.001804915,0.0001723832,0.00009355428,0.000141908,0.0002138167,0.6282712,0.01000761,0.01265448,0.005240308,0.3410637],"study_design_scores_gemma":[0.000006689801,0.00002016224,0.00008333047,0.000007546275,0.000004557921,0.00003784825,0.00001366363,0.9935854,0.001329478,0.004242755,0.000662117,0.000006606921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01073338,0.0001382869,0.9865351,0.0001074829,0.00001791903,0.00004893922,0.000154378,0.001668475,0.0005959198],"genre_scores_gemma":[0.2858617,0.0002468264,0.708358,0.0003100191,0.00005616765,0.0002504181,0.002091796,0.0005968155,0.002228207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007104224,"threshold_uncertainty_score":0.0141257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0109086891132063,"score_gpt":0.2493633412178731,"score_spread":0.2384546521046668,"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."}}