{"id":"W2158175288","doi":"10.1109/icpr.2006.44","title":"A Clustering-based Algorithm for Extracting the Centerlines of 2D and 3D Objects","year":2006,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Cluster analysis; Computer science; Object (grammar); Set (abstract data type); Artificial intelligence; Computer vision; Cluster (spacecraft); Algorithm; Pattern recognition (psychology); Data set","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008403182,0.00006542786,0.00009459882,0.00003462211,0.00003873773,0.00001769572,0.00004050972,0.00002346619,0.000006115737],"category_scores_gemma":[0.000005309539,0.00004500365,0.0000470134,0.0000485458,0.00001055928,0.00002585657,0.000006688157,0.0000336263,4.350564e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005352723,"about_ca_system_score_gemma":0.000003400464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000824292,"about_ca_topic_score_gemma":0.0001769194,"domain_scores_codex":[0.9996439,0.000004378782,0.0001327081,0.00006913761,0.00004905782,0.0001008692],"domain_scores_gemma":[0.9997882,0.00008018462,0.00001883668,0.00007624792,0.00002417696,0.00001231356],"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":[0.000002892659,0.00001995227,0.0002020181,0.0001004462,0.00003752821,6.9938e-7,0.00007832071,0.8296121,0.001466889,0.000006672944,0.0001956577,0.1682768],"study_design_scores_gemma":[0.0001752982,0.00000843859,0.00005305095,0.0000173948,0.00002521232,8.943977e-7,0.00007064189,0.9974189,0.00201591,0.00002318866,0.0001356681,0.00005536404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0214728,0.0003473658,0.977293,0.00004264037,0.0000434908,0.00005884825,0.000006029094,0.00007917567,0.0006566098],"genre_scores_gemma":[0.9477326,0.000007323992,0.05198904,0.00002024186,0.00007646315,0.000007529343,0.000005017187,0.00001225455,0.0001495022],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9262598,"threshold_uncertainty_score":0.1835195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00867177323511459,"score_gpt":0.2110873857092057,"score_spread":0.2024156124740911,"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."}}