{"id":"W2596825651","doi":"10.3917/riges.421.0076","title":"Intelligence artificielle : une mine d’or pour les entreprises","year":2017,"lang":"fr","type":"article","venue":"Gestion","topic":"Competitive and Knowledge Intelligence","field":"Business, Management and Accounting","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Humanities; Gynecology; Philosophy; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.009320997,0.001555666,0.001936932,0.007974298,0.003100441,0.01434749,0.002469491,0.005710898,0.01489656],"category_scores_gemma":[0.03964074,0.0007885251,0.001350904,0.007661222,0.0158098,0.03120952,0.004927785,0.007598505,0.003577469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003503474,"about_ca_system_score_gemma":0.002734315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003302334,"about_ca_topic_score_gemma":0.002204015,"domain_scores_codex":[0.9932974,0.00241476,0.0005675119,0.001025956,0.002379136,0.0003152672],"domain_scores_gemma":[0.9762043,0.01418716,0.001699105,0.003503824,0.003752277,0.0006531536],"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":[0.00007631745,0.00003205095,0.001134282,0.0004761627,0.0000578341,0.0001373844,0.000709109,0.0006798435,0.0002030036,0.891879,0.02435348,0.08026158],"study_design_scores_gemma":[0.00001108699,0.00001826987,0.000372293,0.0004663842,0.00002490263,0.0001597117,0.0004987098,0.002339897,0.0001788838,0.8904755,0.1054267,0.00002771511],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01705586,0.1809687,0.2684858,0.3119607,0.01119982,0.0001492386,0.001698931,0.0008476942,0.2076333],"genre_scores_gemma":[0.5680131,0.1248459,0.1569255,0.03780228,0.0261997,0.000568167,0.001412157,0.000776295,0.08345696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01489656,"threshold_uncertainty_score":0.04983401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06131167550375215,"score_gpt":0.2899191123651164,"score_spread":0.2286074368613642,"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."}}