{"id":"W2049283648","doi":"10.1007/s11042-006-0002-x","title":"An efficient and robust algorithm for 3D mesh segmentation","year":2006,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Segmentation; Algorithm; Curvature; Scale-space segmentation; Artificial intelligence; Partition (number theory); Segmentation-based object categorization; Polygon mesh; Image segmentation; Process (computing); Pattern recognition (psychology); Computer vision; Mathematics","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":[0.001256997,0.001807839,0.002084409,0.003423088,0.001054474,0.001973256,0.003163895,0.002877342,0.00552776],"category_scores_gemma":[0.003806121,0.001701471,0.001885279,0.002929402,0.001031502,0.002318433,0.002716543,0.0020814,0.003748749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000973978,"about_ca_system_score_gemma":0.001748973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00419169,"about_ca_topic_score_gemma":0.006591099,"domain_scores_codex":[0.9981732,0.0002052107,0.0001202893,0.0003186675,0.001073183,0.0001093745],"domain_scores_gemma":[0.998359,0.000489226,0.0001483265,0.0003560911,0.0005812764,0.00006609985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002074424,0.00007894255,0.0003315232,0.0001584702,0.000129482,0.0001415079,0.00009238443,0.0661572,0.06150381,0.008127012,0.008590457,0.8544816],"study_design_scores_gemma":[0.00004242175,0.00007200221,0.0003941686,0.00002124632,0.00005618866,0.0003960201,0.00002552387,0.9494755,0.03175347,0.006903717,0.01080595,0.00005390639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001019303,0.0001220263,0.9971817,0.00003836895,0.00004431161,0.00003477696,0.00003085314,0.001197628,0.000331062],"genre_scores_gemma":[0.01178096,0.00014346,0.9859562,0.00005631003,0.00004338332,0.00008264631,0.0002039009,0.0003594511,0.001373641],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00552776,"threshold_uncertainty_score":0.01849222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01341504190496097,"score_gpt":0.2349736302280127,"score_spread":0.2215585883230517,"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."}}