{"id":"W2482570748","doi":"10.4018/ijbir.2016010101","title":"Business Intelligence and Analytics Research","year":2016,"lang":"en","type":"article","venue":"International Journal of Business Intelligence Research","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Business intelligence; Sociotechnical system; Computer science; Analytics; Process (computing); Heuristic; Knowledge management; Big data; Data science; Business analytics; Constraint (computer-aided design); Management science; Artificial intelligence; Business model; Business; Data mining; Marketing; Business analysis","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":["metaresearch","metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.008949513,0.0003833386,0.0005378315,0.004303198,0.0004373767,0.001420568,0.003745828,0.0002552471,0.001427654],"category_scores_gemma":[0.01149074,0.0002590537,0.0001336481,0.005668261,0.001958552,0.004449143,0.001855165,0.001062586,0.0009349156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003380154,"about_ca_system_score_gemma":0.0005280934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008412112,"about_ca_topic_score_gemma":0.0001453383,"domain_scores_codex":[0.9920619,0.0001662051,0.001457413,0.000708873,0.004516209,0.001089438],"domain_scores_gemma":[0.9623104,0.001654183,0.0006088805,0.0006447553,0.03466808,0.00011365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001043191,0.0006889902,0.0143912,0.0003127474,0.0002669135,0.0006931321,0.0001508719,0.0005503333,0.007100828,0.1211239,0.0109261,0.8427518],"study_design_scores_gemma":[0.00104022,0.0002235475,0.05268064,0.006917397,0.000125664,0.001532358,0.003455089,0.005559471,0.02021855,0.3127466,0.5936509,0.001849586],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1915155,0.003885578,0.6965302,0.07522276,0.01100066,0.00114683,0.00006766603,0.0001854016,0.02044539],"genre_scores_gemma":[0.9872241,0.005714205,0.0008635752,0.0002596977,0.004886205,0.00001661992,0.000009168434,0.00006620327,0.000960209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8409022,"threshold_uncertainty_score":0.9999862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3507662412572711,"score_gpt":0.4536023237183622,"score_spread":0.1028360824610912,"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."}}