{"id":"W2129821487","doi":"10.1109/icpr.1992.201788","title":"Automatic evaluation of skeleton shapes","year":2003,"lang":"en","type":"article","venue":"","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Skeletonization; Matching (statistics); Computer science; Skeleton (computer programming); Artificial intelligence; Quality (philosophy); Data mining; Engineering drawing; Information retrieval; Pattern recognition (psychology); Mathematics; Engineering; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006170355,0.00003445373,0.00005024539,0.0000501572,0.00003327111,0.00003173551,0.0001047352,0.00001787611,0.0001322437],"category_scores_gemma":[0.00008987325,0.00002791439,0.00001767316,0.0001621469,0.00001661978,0.000255054,0.000009105819,0.00002178796,0.0000182672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000124943,"about_ca_system_score_gemma":0.0001291649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000329969,"about_ca_topic_score_gemma":0.000001090696,"domain_scores_codex":[0.9994355,0.00007201248,0.0001070095,0.00009546256,0.0002277429,0.0000623131],"domain_scores_gemma":[0.9996689,0.00001643614,0.00005172894,0.00009915973,0.0001496569,0.00001411484],"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":[7.789378e-8,0.00001457146,0.0001338031,0.00001356694,0.000003165095,6.429341e-8,0.0001238417,0.00006251057,0.001086351,0.02329082,0.00009186338,0.9751794],"study_design_scores_gemma":[0.0001799582,0.00001483774,0.0008083793,0.00001475714,0.000008106937,0.00002117093,0.00002760257,0.9332744,0.0411756,0.0243069,0.0001155597,0.00005275884],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.226247,0.000151034,0.7075588,0.0000920876,0.0002519585,0.0000759778,5.237845e-8,0.0001201413,0.0655029],"genre_scores_gemma":[0.8818954,9.235758e-7,0.1179415,0.00001986289,0.000005068119,0.000002931059,9.194716e-8,0.000001258956,0.0001329358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9751266,"threshold_uncertainty_score":0.1447976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02470345467257652,"score_gpt":0.2776042922912268,"score_spread":0.2529008376186503,"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."}}