{"id":"W1979699444","doi":"10.1145/1274000.1274070","title":"Exploring medical data using visual spaces with genetic programming and implicit functional mappings","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Genetic programming; Computer science; Functional reactive programming; Scalar (mathematics); Reactive programming; Functional programming; Extension (predicate logic); Theoretical computer science; Set (abstract data type); Inductive programming; Artificial intelligence; Machine learning; Programming paradigm; Mathematics; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.000349485,0.00008978449,0.00007547114,0.00006835514,0.0002390503,0.0001198316,0.0003987572,0.00002884866,0.00001535465],"category_scores_gemma":[0.00001012573,0.000071345,0.0000091635,0.0003347993,0.0000748552,0.0007654991,0.0005055373,0.00009164897,0.000004322417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001870419,"about_ca_system_score_gemma":0.00008267417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001167621,"about_ca_topic_score_gemma":0.0000365289,"domain_scores_codex":[0.9988024,0.000007944281,0.000150654,0.0004096272,0.0003829304,0.000246396],"domain_scores_gemma":[0.9993481,0.00006905632,0.00003962479,0.0003319811,0.00004765926,0.0001635744],"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":[0.00001785149,0.0003380498,0.02954226,0.00004198687,0.00008699053,0.0000829296,0.0005458968,0.0002871894,0.001930906,0.1031388,0.0002900695,0.8636971],"study_design_scores_gemma":[0.0004753313,0.0001163232,0.1671282,0.00005163273,0.00001524306,0.0008557005,0.0005013824,0.8156883,0.0002009601,0.0004900816,0.01410993,0.0003669599],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2271211,0.0000726139,0.7719542,0.0005341263,0.00004050381,0.00008473494,5.734595e-7,0.0000900039,0.0001022047],"genre_scores_gemma":[0.400019,0.00001717482,0.5996017,0.0001090212,0.0001934733,0.00001263834,0.000007131855,0.000006299137,0.00003349261],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8633301,"threshold_uncertainty_score":0.2909364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1243827251379411,"score_gpt":0.3095728765293999,"score_spread":0.1851901513914588,"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."}}