{"id":"W2245186472","doi":"","title":"ADVERSE EFFECTS OF MERGERS ON AIRLINE PERFORMANCE: THE CASE OF CANADIAN AIRLINES INTERNATIONAL, LTD","year":2001,"lang":"en","type":"article","venue":"","topic":"Aviation Industry Analysis and Trends","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Business; Mergers and acquisitions; Interlining; Aviation; International trade; Finance; Engineering","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":[0.002154071,0.0003229681,0.0003056783,0.002034119,0.004430643,0.002610213,0.0007976894,0.001609034,0.001774416],"category_scores_gemma":[0.009024525,0.0001496157,0.0004162,0.003721436,0.001830167,0.001005178,0.001154957,0.001740282,0.0001720806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02414247,"about_ca_system_score_gemma":0.01381175,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.927741,"about_ca_topic_score_gemma":0.9541855,"domain_scores_codex":[0.9974956,0.0003558099,0.00007010604,0.0001058199,0.0007905206,0.001182018],"domain_scores_gemma":[0.9912146,0.001326508,0.002699994,0.0002102343,0.003050846,0.001497731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001041967,0.0005710209,0.8562162,0.00009971639,0.0002060638,0.006977447,0.01437655,0.01289837,0.001798188,0.02445696,0.02098508,0.06037242],"study_design_scores_gemma":[0.00005361134,0.0002195429,0.9541629,0.00007067113,0.000160178,0.000625521,0.02029705,0.004780595,0.0006772092,0.001789959,0.01707064,0.00009222925],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9763522,0.001050074,0.00005921286,0.004565221,0.00001122963,0.00001879549,0.0002760026,0.000005919387,0.01766135],"genre_scores_gemma":[0.9978036,0.0006774511,0.00003915064,0.0001589991,0.00001541068,0.000003504963,0.000106154,0.000001573904,0.001194024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07225901,"threshold_uncertainty_score":0.1751667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02409728921364659,"score_gpt":0.2235955672232371,"score_spread":0.1994982780095905,"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."}}