{"id":"W3121767947","doi":"","title":"Market Efficiency in Specialist Markets Before and After Automation","year":2001,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Exploit; Automation; Stock exchange; Stock (firearms); Nonparametric statistics; Efficient-market hypothesis; Market efficiency; Econometrics; Business; Stock market; Economics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002424066,0.0001443188,0.0004054882,0.00118315,0.000236791,0.001196005,0.0002638902,0.0003928939,0.001910775],"category_scores_gemma":[0.01684446,0.00009089154,0.0003318935,0.0007953558,0.001012636,0.001634183,0.000618862,0.0003462307,0.0001994941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006086598,"about_ca_system_score_gemma":0.0003643944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002126288,"about_ca_topic_score_gemma":0.001780917,"domain_scores_codex":[0.9990535,0.0002028938,0.00007824359,0.000143254,0.0002966128,0.000225365],"domain_scores_gemma":[0.9762375,0.00988959,0.008605747,0.002263245,0.001883026,0.001120945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003528874,0.0007638999,0.7889555,0.00009926624,0.0003560994,0.0008640994,0.002788047,0.04889336,0.04046798,0.01834943,0.0009473835,0.09398613],"study_design_scores_gemma":[0.0000432086,0.0006080256,0.9625182,0.000004768471,0.00003834369,0.0001796479,0.0003289119,0.02517691,0.006047351,0.004558036,0.0004563035,0.0000402373],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985127,0.00002913337,0.0005957457,0.00001950319,0.000001007205,0.000004053377,0.00002952033,0.00001062657,0.0007977514],"genre_scores_gemma":[0.9996859,0.000005218697,0.0001308064,0.000002655306,0.000002712089,0.00000131559,0.00003159505,0.000001775196,0.000137965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002424066,"threshold_uncertainty_score":0.01281983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007407079548870789,"score_gpt":0.1980543179333874,"score_spread":0.1906472383845166,"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."}}