{"id":"W2620986503","doi":"","title":"Pharmaceutical M&A Activity: Effects on Prices, Innovation, and Competition","year":2017,"lang":"en","type":"article","venue":"","topic":"Pharmaceutical Economics and Policy","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Upstream (networking); Downstream (manufacturing); Competition (biology); Pharmaceutical industry; Harm; Industrial organization; Business; Market concentration; Marketing; Politics; Product (mathematics); Distribution (mathematics); Market structure; Political science; Biotechnology; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0003060288,0.0001130273,0.0002213082,0.0001501789,0.0002961422,0.0002428637,0.0001643947,0.00006747032,0.0002946957],"category_scores_gemma":[0.0001599637,0.0001245952,0.00002977944,0.00005671855,0.00008541687,0.0003030831,0.00008732441,0.0001580971,0.000621449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004297874,"about_ca_system_score_gemma":0.000008444697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000404354,"about_ca_topic_score_gemma":0.000005000893,"domain_scores_codex":[0.9992548,0.00000609094,0.0002325707,0.0002852448,0.00001733491,0.0002040203],"domain_scores_gemma":[0.9992972,0.0000827593,0.0002126276,0.0002993218,0.00001613172,0.00009193068],"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":[0.00001960698,0.00004936638,0.006025028,0.0000295965,0.00002170196,0.000001477373,0.00001544866,9.050235e-7,0.0001804271,0.9805025,0.0006802424,0.01247363],"study_design_scores_gemma":[0.002096023,0.0001071872,0.1524912,0.00002231583,0.000008078755,0.00000544893,0.000002472322,0.007289148,0.00961717,0.2764096,0.5515167,0.0004346622],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7360349,0.00008761253,0.0009204833,0.01438624,0.0003574298,0.0001782254,0.00003510253,0.00003317689,0.2479668],"genre_scores_gemma":[0.9935071,0.0001718649,0.0001696744,0.005297486,0.0002126264,0.00001457097,0.000003921249,0.0000129682,0.000609814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7040929,"threshold_uncertainty_score":0.7987676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08957918890393224,"score_gpt":0.3404639866273215,"score_spread":0.2508847977233892,"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."}}