{"id":"W2151166864","doi":"10.1109/icwapr.2011.6014505","title":"Distinctive parts for shape classification","year":2011,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Object (grammar); Computer science; Artificial intelligence; Set (abstract data type); Shape analysis (program analysis); Pattern recognition (psychology); Feature (linguistics); Type (biology); Topological skeleton; Active shape model; Skeleton (computer programming); Computer vision; Segmentation","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":[0.0001424496,0.00006331842,0.0000609387,0.00003591419,0.00007735821,0.00004103256,0.0003856237,0.00003543066,0.00007644268],"category_scores_gemma":[0.00004004603,0.00005045634,0.00004227875,0.0001485403,0.00003249271,0.0003188562,0.00004380295,0.00003090076,0.00005466027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001868255,"about_ca_system_score_gemma":0.00002336794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004884125,"about_ca_topic_score_gemma":9.181006e-7,"domain_scores_codex":[0.9994426,0.00001480734,0.000126131,0.0002265541,0.0000687461,0.0001211189],"domain_scores_gemma":[0.9994208,0.00003852031,0.0000595522,0.0002978142,0.0001404144,0.00004286696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005664624,0.00005169291,0.0001568978,0.000004061502,0.000003393645,2.137391e-7,0.0002135967,3.211518e-9,0.001884646,0.8272815,0.0009379783,0.1694603],"study_design_scores_gemma":[0.0003569644,0.0003244609,0.06223513,0.00001154937,0.00001234173,0.000006902581,0.0001398753,0.1821516,0.4946826,0.2148428,0.04478279,0.0004529896],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001891292,0.000009233479,0.9566877,0.0003873074,0.00007502991,0.0002180387,0.000001469055,0.0004120915,0.04201997],"genre_scores_gemma":[0.8203233,0.000004503136,0.1779666,0.0001821222,0.00002320765,0.0001245927,0.000003193014,0.000004318299,0.001368195],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8201342,"threshold_uncertainty_score":0.205755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1111865133362468,"score_gpt":0.2860735297389845,"score_spread":0.1748870164027377,"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."}}