{"id":"W2899419147","doi":"10.22215/timreview/1189","title":"Strategic Foresight of Future B2B Customer Opportunities through Machine Learning","year":2018,"lang":"en","type":"article","venue":"Technology Innovation Management Review","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"FedDev Ontario","keywords":"Futures studies; Business; Industrial organization; Field (mathematics); Knowledge management; Marketing; Process management; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"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":[0.002286553,0.0007544413,0.0004266355,0.002718329,0.0003180699,0.002506493,0.001176633,0.0008259487,0.006417437],"category_scores_gemma":[0.006187152,0.0002048076,0.0005986776,0.001996068,0.0002969712,0.002957711,0.0007921669,0.001783459,0.001484446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009897975,"about_ca_system_score_gemma":0.001303603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00927879,"about_ca_topic_score_gemma":0.009992901,"domain_scores_codex":[0.9993772,0.0001967422,0.00004018486,0.0001110444,0.000196667,0.00007820297],"domain_scores_gemma":[0.9964306,0.002249697,0.0005272122,0.0001378764,0.0005354594,0.0001191975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001408419,0.0002154939,0.02520511,0.0007051845,0.0001842862,0.0001005547,0.000126468,0.142347,0.0003861808,0.0170485,0.02904771,0.7844926],"study_design_scores_gemma":[0.00002143222,0.0001081468,0.01122874,0.0009183535,0.00009268369,0.0001424448,0.000257779,0.8879539,0.001276332,0.06228185,0.03564278,0.00007550453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1816559,0.1196224,0.5654702,0.02754837,0.001153567,0.0004332065,0.007432347,0.00340345,0.09328059],"genre_scores_gemma":[0.8822148,0.02955623,0.07623535,0.0007231576,0.000806416,0.0001320833,0.003254737,0.00007879341,0.006998304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00927879,"threshold_uncertainty_score":0.0214684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.147875993223657,"score_gpt":0.3279330196588876,"score_spread":0.1800570264352306,"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."}}