{"id":"W34363959","doi":"10.1016/j.neuroimage.2021.118457","title":"Training Activation Function in Neuro-Wavelet Parametric Modeling","year":2000,"lang":"en","type":"article","venue":"Information Processing and Management of Uncertainty","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Tax Foundation; Ontario Brain Institute","keywords":"Computer science; Wavelet; Parametric statistics; Artificial intelligence; Function (biology); Activation function; Pattern recognition (psychology); Artificial neural network; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001580749,0.0005925665,0.0004797873,0.0006237366,0.0002060261,0.000736538,0.0007995897,0.001079908,0.001433653],"category_scores_gemma":[0.006101262,0.0004127432,0.000721637,0.0008260793,0.0004972398,0.000632339,0.0006578452,0.001223851,0.0004994493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004659522,"about_ca_system_score_gemma":0.0004598658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003331485,"about_ca_topic_score_gemma":0.003066121,"domain_scores_codex":[0.9996604,0.0001964108,0.00001412002,0.0000485509,0.0000516229,0.00002897465],"domain_scores_gemma":[0.9987685,0.001007146,0.00007071003,0.00007402196,0.00006182426,0.00001782698],"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":[0.00003032349,0.0000278047,0.0007365989,0.00004781231,0.00004500762,0.00005727402,0.00004863561,0.9551879,0.0020297,0.007325501,0.0003307291,0.03413271],"study_design_scores_gemma":[9.596945e-7,0.000005000742,0.0001347623,0.00000347433,0.000002326941,0.000007118279,0.000002956423,0.9975375,0.0002209381,0.001879856,0.0002027996,0.000002351316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01647456,0.0002406163,0.9819338,0.0001307033,0.00001482637,0.00003506154,0.00008407418,0.0002466049,0.0008398587],"genre_scores_gemma":[0.7162913,0.000805803,0.2776001,0.00008697155,0.00004340032,0.0005434395,0.0003326095,0.0002664819,0.004029859],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003331485,"threshold_uncertainty_score":0.008359849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03121091538374744,"score_gpt":0.248228420669685,"score_spread":0.2170175052859375,"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."}}