{"id":"W2091315864","doi":"10.1111/j.1540-6288.2000.tb01422.x","title":"Market Efficiency in Specialist Markets Before and After Automation","year":2000,"lang":"en","type":"article","venue":"Financial Review","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Exploit; Automation; Stock exchange; Stock (firearms); Nonparametric statistics; Market efficiency; Econometrics; Efficient-market hypothesis; Business; Economics; Stock market; Financial economics; Industrial organization; Finance; Computer science; Engineering; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007640666,0.0001948395,0.0005300679,0.0001184363,0.0000699985,0.00005630555,0.0001390941,0.00009648249,0.008791052],"category_scores_gemma":[0.000246603,0.0002042504,0.00009244728,0.0003971986,0.00008122037,0.0003226219,0.00002950377,0.0001285701,0.0004323598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006829746,"about_ca_system_score_gemma":0.00003885181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008160187,"about_ca_topic_score_gemma":0.0001629354,"domain_scores_codex":[0.9984594,0.00003282139,0.0007422643,0.0004007681,0.00004981555,0.0003149556],"domain_scores_gemma":[0.9995068,0.00002088885,0.000157778,0.0002391423,0.00001408718,0.00006129508],"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":[0.0001756488,0.0003150662,0.09054102,0.003894315,0.00001023294,0.00008205161,0.0003531856,0.000001197109,6.27907e-7,0.39836,0.04844211,0.4578245],"study_design_scores_gemma":[0.0001729721,0.00003839441,0.5882218,0.0005895577,0.000003494862,0.000003190693,8.730753e-7,0.00008094836,2.634771e-7,0.01500381,0.3957291,0.0001555605],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3566374,0.1703947,0.00003696589,0.001982746,0.0004408284,0.001101701,0.0001660211,0.00006447123,0.4691751],"genre_scores_gemma":[0.7988626,0.1858381,0.0005427568,0.004891701,0.0004139213,0.000317618,0.00003456403,0.00004605954,0.00905267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4976808,"threshold_uncertainty_score":0.992115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01268926482199427,"score_gpt":0.2169385357625409,"score_spread":0.2042492709405466,"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."}}