{"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":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.00174932,0.00007328429,0.0002023902,0.002626763,0.00009250689,0.0001751639,0.0006067806,0.00006308553,0.000138743],"category_scores_gemma":[0.005712245,0.00006229361,0.00003155597,0.0212874,0.0001714486,0.0002558003,0.0004272159,0.000141593,0.00003199315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003589704,"about_ca_system_score_gemma":0.0001406779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005105921,"about_ca_topic_score_gemma":0.00001167826,"domain_scores_codex":[0.9977589,0.0001565022,0.0004845859,0.0002747949,0.001169218,0.0001560147],"domain_scores_gemma":[0.9943495,0.0006207896,0.0003044303,0.0001759194,0.004510712,0.0000386842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001623493,0.0002450119,0.5686629,0.00009553738,0.0006559769,0.00001007479,0.0006247541,0.003254407,0.008425631,0.2019114,0.006708845,0.2092431],"study_design_scores_gemma":[0.0007168818,0.00008607697,0.5822821,0.00007431117,0.0001737029,0.000003608855,0.0008781651,0.1898197,0.003272777,0.2026248,0.01979105,0.0002768712],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8537004,0.000174525,0.1360843,0.005646625,0.0003835915,0.0002184042,0.000002268956,0.00002312367,0.003766757],"genre_scores_gemma":[0.9972262,0.00001584003,0.001462768,0.0001126797,0.0002386866,0.00001084478,0.00001560791,0.000006245799,0.0009110693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2089662,"threshold_uncertainty_score":0.9995156,"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."}}