{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008453751,0.000664465,0.0004847913,0.0009097985,0.0002622051,0.001478476,0.0009209693,0.0007755362,0.005094503],"category_scores_gemma":[0.003673744,0.0003158941,0.0009755195,0.001099787,0.0006970598,0.0008994263,0.001055793,0.001087724,0.0006993389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006925179,"about_ca_system_score_gemma":0.000644628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003496171,"about_ca_topic_score_gemma":0.003595276,"domain_scores_codex":[0.9995643,0.000216973,0.00001428947,0.00008059876,0.0001027332,0.00002109256],"domain_scores_gemma":[0.9990983,0.0007006573,0.00003288818,0.00005570351,0.00007563736,0.0000368084],"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":[0.0001546496,0.0001000624,0.001805023,0.0002437619,0.0001278784,0.0001876068,0.0002450988,0.510359,0.006961123,0.07172085,0.007286652,0.4008083],"study_design_scores_gemma":[0.00002554554,0.00002645768,0.0002130171,0.00003077152,0.00001325292,0.0000692914,0.0000692336,0.94045,0.002097416,0.05351545,0.003476666,0.00001296134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01193624,0.0004060722,0.9819171,0.0006577938,0.00003847468,0.0000745716,0.0002279327,0.000708617,0.004033222],"genre_scores_gemma":[0.1629705,0.0007301105,0.8307016,0.0002106624,0.0000560524,0.0002443477,0.0006017794,0.0002243386,0.004260665],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005094503,"threshold_uncertainty_score":0.01704282,"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."}}