{"id":"W2495563369","doi":"10.1142/9789814343008_0007","title":"NONPARAMETRIC SAMPLE-BASED METHODS FOR IMAGE UNDERSTANDING","year":2011,"lang":"en","type":"book-chapter","venue":"Series in computer vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Nonparametric statistics; Sample (material); Image (mathematics); Computer science; Artificial intelligence; Statistics; Mathematics; Pattern recognition (psychology); Chemistry; Chromatography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001207265,0.000451843,0.0006009848,0.001023474,0.0001244609,0.0003013084,0.001359641,0.0003749966,0.0001587633],"category_scores_gemma":[0.0001470697,0.0004434265,0.0002254642,0.000254446,0.0002360758,0.0007662279,0.0006448152,0.0004254855,0.00001929878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004280106,"about_ca_system_score_gemma":0.0001462728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001361141,"about_ca_topic_score_gemma":0.000004434721,"domain_scores_codex":[0.997456,0.0001289515,0.000721079,0.0009161679,0.0003837652,0.0003941013],"domain_scores_gemma":[0.9966891,0.001662646,0.0003524816,0.0009871108,0.0001563115,0.0001523195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004361232,0.00003998396,0.000001246962,0.0001835481,0.00002175062,0.00002966002,0.0001739579,0.000007034573,0.00009501584,0.2604673,0.003382201,0.7355547],"study_design_scores_gemma":[0.0008858005,0.00146174,0.000006772024,0.0007935597,0.00002481305,0.00002098608,0.000004383174,0.08935669,0.006710771,0.8750213,0.02477989,0.0009333441],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[1.67568e-7,0.0001041703,0.9868189,0.0002179743,0.001099915,0.0008865808,0.00001761571,0.0004518903,0.01040283],"genre_scores_gemma":[0.00002761407,0.00008371059,0.995793,0.0007179408,0.0001433188,0.00004704013,0.00005324258,0.00006285728,0.003071247],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7346213,"threshold_uncertainty_score":0.9998018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08641229262845973,"score_gpt":0.3803471155718827,"score_spread":0.293934822943423,"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."}}