{"id":"W2105314145","doi":"10.1007/s00371-011-0640-5","title":"Reeb graph path dissimilarity for 3D object matching and retrieval","year":2011,"lang":"en","type":"article","venue":"The Visual Computer","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Skeletonization; Mathematics; Topological skeleton; Graph; Pattern recognition (psychology); Lattice graph; 3-dimensional matching; Artificial intelligence; Computer science; Topology (electrical circuits); Algorithm; Voltage graph; Line graph; Combinatorics; Bipartite graph; Segmentation; Active shape model","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009069212,0.0005205386,0.001288297,0.004045883,0.0005919306,0.0011981,0.001538787,0.001219936,0.005249212],"category_scores_gemma":[0.004499559,0.0003351849,0.0007441988,0.003807599,0.0005821114,0.002207403,0.00107836,0.0008078426,0.001580686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007172973,"about_ca_system_score_gemma":0.0007372118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00280532,"about_ca_topic_score_gemma":0.00551685,"domain_scores_codex":[0.9988171,0.0002311172,0.00006493601,0.0002117575,0.0006030796,0.00007213114],"domain_scores_gemma":[0.9987928,0.0003448925,0.0001109841,0.0003613114,0.000328385,0.00006167718],"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":[0.0007795428,0.0002731922,0.00190726,0.0003163846,0.000155725,0.0001655516,0.0001494418,0.05121866,0.06423649,0.03914269,0.008026151,0.8336288],"study_design_scores_gemma":[0.0000928943,0.0002483497,0.003328263,0.00002880312,0.00007480678,0.0007202044,0.000163287,0.9052479,0.03833303,0.03625102,0.01544805,0.00006348849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03602067,0.0007548911,0.9594318,0.0001231347,0.00007129809,0.0001250366,0.0003855382,0.001320025,0.00176758],"genre_scores_gemma":[0.3312957,0.0006113435,0.6602455,0.0001213998,0.000102902,0.0002043766,0.001467238,0.0005296432,0.005421767],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005249212,"threshold_uncertainty_score":0.0175603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02690597287329391,"score_gpt":0.2465975928823463,"score_spread":0.2196916200090524,"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."}}