{"id":"W4362468670","doi":"10.3390/computers12040072","title":"Artificial Intelligence and Knowledge Management: Impacts, Benefits, and Implementation","year":2023,"lang":"en","type":"article","venue":"Computers","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Canada West","funders":"","keywords":"Documentation; Dissemination; Knowledge management; Process (computing); Computer science; Data science","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":[0.0002294594,0.0001476573,0.000118222,0.0003449643,0.0001929677,0.0004221393,0.000176738,0.000034845,0.00005207727],"category_scores_gemma":[0.000009155303,0.0001430762,0.00002098195,0.0007466523,0.00006994593,0.0007656806,0.0005433686,0.00005895107,0.0003409006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001028148,"about_ca_system_score_gemma":0.00000528734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000800603,"about_ca_topic_score_gemma":0.00008607313,"domain_scores_codex":[0.9990886,0.000004911229,0.0002145073,0.0003141684,0.0001132277,0.0002645862],"domain_scores_gemma":[0.9996665,0.00003652546,0.00007232276,0.0001558215,0.00004991888,0.00001892321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000008159444,0.00001542844,0.002935776,0.0002250469,0.00001705482,0.000006254413,0.00009062991,0.00003367915,0.00002293284,0.1489491,0.005390881,0.842305],"study_design_scores_gemma":[0.0006536998,0.00008291272,0.3907122,0.0009427979,0.0003471476,0.00003574375,0.007142378,0.1709325,0.001216593,0.06778457,0.3578395,0.002309992],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9482003,0.002479572,0.03277062,0.003405291,0.003267393,0.001134252,0.00003461394,0.001096316,0.007611575],"genre_scores_gemma":[0.9976895,0.0007465865,0.0004261724,0.0004285726,0.0005205222,0.00001261816,0.0001239158,0.00001859765,0.00003350851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.839995,"threshold_uncertainty_score":0.5834477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09929015019965226,"score_gpt":0.3261512449751767,"score_spread":0.2268610947755245,"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."}}