{"id":"W3107823784","doi":"10.5539/ibr.v13n12p51","title":"Effects of Innovation Education and Corporate Needs -Analysis Using Bayesian Network","year":2020,"lang":"en","type":"article","venue":"International Business Research","topic":"Knowledge Management and Technology","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Creativity; Innovator; Bayesian network; Odds; Skepticism; Set (abstract data type); Analytical skill; Psychology; Knowledge management; Computer science; Mathematics education; Artificial intelligence; Social psychology; Machine learning; Intellectual property","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":[0.01439242,0.0006106386,0.0007620433,0.003131388,0.0006277807,0.001451932,0.0008638255,0.001088311,0.007578446],"category_scores_gemma":[0.08494414,0.000371286,0.002178544,0.001783818,0.0008308452,0.00283198,0.001534099,0.001338471,0.0002570059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001643302,"about_ca_system_score_gemma":0.001230644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008894815,"about_ca_topic_score_gemma":0.004041153,"domain_scores_codex":[0.9855398,0.01083439,0.0004625201,0.00121355,0.001392721,0.0005570573],"domain_scores_gemma":[0.8535437,0.1381404,0.0037446,0.001686268,0.001937997,0.0009469538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002914404,0.001900931,0.757323,0.0004794486,0.002098858,0.0005955735,0.003755999,0.08301474,0.001166186,0.02407311,0.0009925165,0.1216851],"study_design_scores_gemma":[0.0001986805,0.001535172,0.3859579,0.0001452625,0.001625812,0.0004104868,0.004260575,0.5605415,0.00127524,0.0420195,0.001909816,0.0001201334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9532529,0.0002355903,0.03882606,0.0009445828,0.00002341216,0.0003047823,0.0003658274,0.00007881172,0.005968083],"genre_scores_gemma":[0.9925074,0.0001172045,0.006583026,0.00003114401,0.000009992412,0.0001441807,0.0001207901,0.000009422421,0.0004769783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01439242,"threshold_uncertainty_score":0.07611531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2210906055528978,"score_gpt":0.4531245952305217,"score_spread":0.2320339896776239,"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."}}