{"id":"W4399860543","doi":"10.5539/cis.v17n2p1","title":"Opportunities for Using Machine Learning and Artificial Intelligence in Business Analytics","year":2024,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Analytics; Business intelligence; Machine learning; Data science; Knowledge management","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":[0.01375259,0.0008804259,0.000800951,0.004101752,0.001208892,0.01073622,0.001359381,0.003650721,0.003445505],"category_scores_gemma":[0.0164284,0.0005341132,0.0006629871,0.00477673,0.008089451,0.02345314,0.004816984,0.005930774,0.001464483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002042036,"about_ca_system_score_gemma":0.002653394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00119932,"about_ca_topic_score_gemma":0.00136226,"domain_scores_codex":[0.991558,0.005172702,0.0003361019,0.0005324476,0.002014627,0.0003861555],"domain_scores_gemma":[0.9712147,0.02231812,0.0009452295,0.001723483,0.002804053,0.0009944506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001667339,0.000224776,0.004627482,0.0009847622,0.0001008764,0.000186474,0.001200568,0.002564497,0.001341236,0.6931792,0.01728643,0.2781369],"study_design_scores_gemma":[0.00002867313,0.0001547384,0.002626905,0.00141023,0.0000307276,0.0002798731,0.001639401,0.01448606,0.001236946,0.7453634,0.2326322,0.0001109233],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.03009726,0.3015482,0.1610106,0.3348903,0.00311259,0.0001483392,0.0003170998,0.0005248736,0.1683508],"genre_scores_gemma":[0.5494401,0.2150292,0.1895685,0.02272136,0.01050356,0.0003030564,0.0003292951,0.0002013392,0.01190345],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01375259,"threshold_uncertainty_score":0.07273155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1775386601981421,"score_gpt":0.3311603269045151,"score_spread":0.153621666706373,"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."}}