{"id":"W1993837825","doi":"10.1016/j.procs.2014.05.383","title":"Supervised Discretization for Optimal Prediction","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Discretization; Artificial intelligence; Machine learning; Mathematical optimization; Mathematics","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":[0.0004577852,0.0001037376,0.00009098009,0.00009354475,0.000428608,0.0004023534,0.001225263,0.0000275026,0.00000131364],"category_scores_gemma":[0.0000350581,0.00009062066,0.0000373883,0.0008815721,0.0001324044,0.001042864,0.0002621759,0.00005629235,0.00001153485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000243033,"about_ca_system_score_gemma":0.00007876476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001222597,"about_ca_topic_score_gemma":5.296357e-7,"domain_scores_codex":[0.9986289,0.0000122943,0.0001734517,0.0005730503,0.0002924375,0.0003199244],"domain_scores_gemma":[0.9990963,0.00008105026,0.00006100283,0.00040408,0.0002246447,0.0001329255],"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":[0.000004859733,0.00008499312,0.0009041356,0.00003081855,0.000003318529,1.951575e-7,0.0005373309,0.01928705,0.004790255,0.4256178,0.002657818,0.5460814],"study_design_scores_gemma":[0.0001706398,0.0001080657,0.002032783,0.000008843343,0.00000197545,0.000005622147,8.876584e-7,0.9884965,0.001659128,0.003963381,0.003443762,0.0001084013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009432249,0.000008446094,0.987833,0.001378515,0.0005542268,0.0003467507,0.000002167693,0.0002644238,0.0001802163],"genre_scores_gemma":[0.5444915,0.000002886356,0.4545588,0.0004076396,0.0004118233,0.00009526194,0.00000455672,0.000005112772,0.00002238246],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9692094,"threshold_uncertainty_score":0.3879903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01054849940146963,"score_gpt":0.2299287971226056,"score_spread":0.219380297721136,"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."}}