{"id":"W3148247211","doi":"","title":"Unilateral Effects from Mergers: The Oracle Case","year":2006,"lang":"en","type":"article","venue":"Chapters","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Business; Oracle; Mergers and acquisitions; Industrial organization; Computer science; Finance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002609944,0.0007192961,0.001121108,0.001058259,0.002547612,0.007205966,0.001025511,0.005861483,0.02524227],"category_scores_gemma":[0.009983433,0.0004470328,0.001053274,0.001403867,0.005802166,0.008693228,0.006207984,0.005229186,0.001931745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001925955,"about_ca_system_score_gemma":0.001706357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006135102,"about_ca_topic_score_gemma":0.00733079,"domain_scores_codex":[0.99726,0.0008322129,0.0001330311,0.0002008933,0.0006892823,0.000884583],"domain_scores_gemma":[0.9952939,0.0031757,0.0004739472,0.0004456492,0.0002510221,0.0003596901],"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":[0.00008530918,0.00002513593,0.0008876984,0.00003770978,0.00001033997,0.001172221,0.0002947401,0.001530423,0.0001823565,0.9872069,0.002610207,0.005956861],"study_design_scores_gemma":[0.0001815272,0.0001235252,0.002210041,0.0001590912,0.000103934,0.001732953,0.001277828,0.006436716,0.000964112,0.9466113,0.04013599,0.00006290311],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1244483,0.004995141,0.01340134,0.0116438,0.0001688679,0.00009786504,0.0001900066,0.0001563279,0.8448983],"genre_scores_gemma":[0.9586825,0.002898947,0.00134301,0.001420147,0.0002688073,0.00005206145,0.00007249942,0.00004314934,0.03521886],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02524227,"threshold_uncertainty_score":0.08444381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02040799680623797,"score_gpt":0.1735529112714834,"score_spread":0.1531449144652454,"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."}}