{"id":"W1579385695","doi":"10.1002/9780470973134.ch9","title":"Genetic Programming for Exploring Medical Data Using Visual Spaces","year":2010,"lang":"en","type":"other","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Genetic programming; Computer science; Artificial intelligence; Data science; Human–computer interaction","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":[0.0001923859,0.0002666338,0.0002390649,0.0002329637,0.0003279119,0.0001443361,0.0008742596,0.0002899871,0.00004434537],"category_scores_gemma":[0.00003741539,0.0002821272,0.00004784795,0.0002600609,0.0001470729,0.000222514,0.0006805121,0.0002142337,0.00001382731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003553998,"about_ca_system_score_gemma":0.000341049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001487229,"about_ca_topic_score_gemma":0.00004047053,"domain_scores_codex":[0.9978967,0.00004677228,0.0003284007,0.0008705474,0.0005182259,0.0003393769],"domain_scores_gemma":[0.9988732,0.0001056239,0.0002154938,0.0005257707,0.00008230602,0.0001975802],"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":[0.000007126787,0.0002898185,0.0006156705,0.0002536034,0.0001512471,0.00001589186,0.0001538278,0.003055983,0.00005747469,0.003383718,0.07427702,0.9177386],"study_design_scores_gemma":[0.0002822601,0.00005260987,0.003020664,0.00008218738,0.00003767776,0.000115237,0.00002310789,0.815423,7.35983e-7,0.001835264,0.1788214,0.0003059199],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001047822,0.004231979,0.9921206,0.000349666,0.0007068644,0.0007038779,0.00005410117,0.0002917938,0.0004932714],"genre_scores_gemma":[0.002231034,0.0004435339,0.9928358,0.00005468338,0.001433619,0.00018109,0.0003415589,0.0001306351,0.002348102],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9174327,"threshold_uncertainty_score":0.9999631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05950827733421983,"score_gpt":0.3158990664913205,"score_spread":0.2563907891571007,"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."}}