{"id":"W1836993545","doi":"","title":"BAYESIAN DECISION THEORY IN SIMILARITY CRITERIA USED IN RANGE IMAGES SEGMENTATION","year":2007,"lang":"en","type":"article","venue":"Revista Ingenierías Universidad de Medellín","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Bayesian probability; Range (aeronautics); Pattern recognition (psychology); Similarity (geometry); Segmentation; Decision theory; Computer science; Mathematics; Statistics; Image (mathematics); Engineering","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":[0.01072389,0.0008728964,0.002983829,0.002756563,0.0009051968,0.003704844,0.002038904,0.002540904,0.001926175],"category_scores_gemma":[0.02958256,0.001420202,0.001718595,0.00247779,0.002630858,0.003533834,0.00201266,0.002347086,0.0003590026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002851793,"about_ca_system_score_gemma":0.001903434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007140209,"about_ca_topic_score_gemma":0.004583885,"domain_scores_codex":[0.9932389,0.003823416,0.0003683305,0.0006126614,0.001681968,0.0002746765],"domain_scores_gemma":[0.9770641,0.02010566,0.0005883661,0.0003292341,0.001634332,0.0002783274],"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":[0.0002323801,0.000114399,0.001001315,0.0003585049,0.0001737213,0.0001657454,0.0002676568,0.6769706,0.002068081,0.2244918,0.001277641,0.09287813],"study_design_scores_gemma":[0.00001436549,0.00003084588,0.0002622099,0.00003662075,0.0000267358,0.00003139345,0.00001716452,0.9517652,0.0004609689,0.04692885,0.000403962,0.00002174169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006388611,0.0005999882,0.9919724,0.0001979617,0.00002237648,0.00002681912,0.00001681701,0.0000312319,0.000743749],"genre_scores_gemma":[0.4955721,0.001968423,0.4976852,0.0002239174,0.0003094528,0.0003148852,0.000194848,0.0001513268,0.003579939],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01072389,"threshold_uncertainty_score":0.056714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01420524458321654,"score_gpt":0.3215990119019639,"score_spread":0.3073937673187473,"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."}}