{"id":"W2599312750","doi":"10.4018/ijcini.2017040104","title":"Towards Developing the Piece-Wise Linear Neural Network Algorithm for Rule Extraction","year":2017,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Sigmoid function; Computer science; Artificial neural network; Algorithm; Fidelity; Nonlinear system; Artificial intelligence","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":[0.0004335776,0.0001187573,0.0001360727,0.00005911161,0.0004814803,0.0006701744,0.001253141,0.00004095904,0.000002812709],"category_scores_gemma":[0.0002277485,0.00007638626,0.0001028404,0.00006622758,0.0001162549,0.001490307,0.0002524748,0.0003013086,0.000003361853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002842044,"about_ca_system_score_gemma":0.00006827583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000654853,"about_ca_topic_score_gemma":0.000003181404,"domain_scores_codex":[0.9988701,0.00001637524,0.0005310277,0.00008068986,0.0003330117,0.0001687387],"domain_scores_gemma":[0.9971613,0.0004468517,0.0008627736,0.0001343471,0.001336411,0.000058312],"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":[0.00001977997,0.00001084268,0.00004458911,0.000003238708,0.0000593243,0.000004521636,0.0002722104,0.0004735193,0.00000841407,0.01092514,0.0001697943,0.9880086],"study_design_scores_gemma":[0.0002562962,0.00008933588,0.003081041,0.0002125535,0.00002136766,0.0003697338,0.0002202664,0.9663878,0.002240254,0.02229005,0.004663469,0.0001678192],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01198642,0.0003319636,0.9823315,0.003136418,0.001832933,0.0001696843,0.000008994643,0.00001011952,0.0001919989],"genre_scores_gemma":[0.8143654,0.0007542519,0.1828075,0.001113416,0.0008929677,0.00001118372,0.000004827317,0.000005266214,0.00004518519],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9878408,"threshold_uncertainty_score":0.6462507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03569953536162607,"score_gpt":0.3503715311612569,"score_spread":0.3146719957996308,"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."}}