{"id":"W3125342939","doi":"10.1111/poms.12707","title":"The Operational Value of Social Media Information","year":2017,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":389,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Social media; Computer science; Sample (material); Random forest; Variety (cybernetics); Marketing; Business; Machine learning; Artificial intelligence; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004875653,0.00104888,0.0004791995,0.003100748,0.0005536756,0.00376747,0.0008750648,0.001279035,0.001732338],"category_scores_gemma":[0.03558348,0.0003093341,0.0004518642,0.003279526,0.0007102868,0.005569003,0.001220359,0.001683719,0.0005814767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009516136,"about_ca_system_score_gemma":0.000596993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008656289,"about_ca_topic_score_gemma":0.007117266,"domain_scores_codex":[0.9959686,0.002204507,0.0002004282,0.0004118607,0.001022722,0.0001918749],"domain_scores_gemma":[0.9484079,0.03969754,0.003598555,0.004159287,0.00360221,0.0005346029],"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":[0.0008294836,0.0005110083,0.2854033,0.0005292369,0.0006460437,0.0004676456,0.0008873318,0.1536394,0.003408734,0.02515109,0.02202372,0.506503],"study_design_scores_gemma":[0.00006519202,0.0003405518,0.1146936,0.0004513433,0.0004036693,0.0003449778,0.002057024,0.7803045,0.01061332,0.05109219,0.0394409,0.0001926801],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.822595,0.007275627,0.07973019,0.02814981,0.001084232,0.0002132859,0.01105646,0.001270325,0.04862506],"genre_scores_gemma":[0.9860376,0.0008884104,0.009766473,0.0002262174,0.0003496696,0.00002208122,0.001969686,0.00003369119,0.0007062355],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008656289,"threshold_uncertainty_score":0.02578527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07901527634363928,"score_gpt":0.3725444339585274,"score_spread":0.2935291576148881,"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."}}