{"id":"W2614136060","doi":"10.5539/ibr.v10n6p137","title":"Designing Optimal Knowledge Base for Neural Expert Systems","year":2017,"lang":"en","type":"article","venue":"International Business Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Knowledge base; Expert system; Artificial neural network; Generalization; Set (abstract data type); Artificial intelligence; Machine learning; Legal expert system; Plan (archaeology); Domain (mathematical analysis); Domain knowledge; Subject-matter expert; Base (topology); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.002210947,0.0004984814,0.001024564,0.001836092,0.0007263747,0.001622192,0.001890812,0.001106795,0.002609881],"category_scores_gemma":[0.007456859,0.0006613065,0.0004510861,0.001007891,0.0005810205,0.002553556,0.001523963,0.001187616,0.0009152898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001162105,"about_ca_system_score_gemma":0.001633943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003547159,"about_ca_topic_score_gemma":0.003916217,"domain_scores_codex":[0.9986632,0.0002945969,0.0001400059,0.000287328,0.0004868494,0.0001280507],"domain_scores_gemma":[0.9977733,0.0007597915,0.0001557953,0.0002183935,0.001038556,0.00005410322],"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":[0.0001594275,0.0002224198,0.001265698,0.0002940973,0.00009141376,0.000290275,0.0002858802,0.5203611,0.01065534,0.0398035,0.004331737,0.4222392],"study_design_scores_gemma":[0.00002246701,0.00004298216,0.0002513417,0.00005788931,0.00003724542,0.0000528916,0.00006681727,0.9677862,0.00704359,0.01974633,0.004876606,0.00001561559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01058088,0.000193116,0.9856651,0.0001412964,0.00002530788,0.0001551772,0.00009035107,0.0004631115,0.00268565],"genre_scores_gemma":[0.2375783,0.0004773068,0.7587864,0.0002043583,0.00005289559,0.0006688173,0.0005437277,0.0000837831,0.001604484],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003547159,"threshold_uncertainty_score":0.01169276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2006564741477611,"score_gpt":0.4449186130065726,"score_spread":0.2442621388588116,"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."}}