{"id":"W3125695141","doi":"10.3390/jrfm13050100","title":"Monthly Art Market Returns","year":2020,"lang":"en","type":"preprint","venue":"Journal of risk and financial management","topic":"Art History and Market Analysis","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Index (typography); Market liquidity; Art market; Econometrics; Economics; Financial economics; Estimation; Econometric model; Monetary economics; Computer science; Humanities; Art","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0009057606,0.0003539828,0.0004347316,0.001971633,0.0001856813,0.001792086,0.0005796672,0.0005041184,0.007482733],"category_scores_gemma":[0.01102226,0.0001598682,0.0003091092,0.00193683,0.0003492531,0.002196805,0.0005517185,0.00069903,0.001424428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003081127,"about_ca_system_score_gemma":0.0001977029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00108665,"about_ca_topic_score_gemma":0.001203894,"domain_scores_codex":[0.9992735,0.0001437735,0.00004527967,0.0001703024,0.0003166309,0.00005057478],"domain_scores_gemma":[0.9969918,0.0009739716,0.0009606458,0.000533541,0.00038412,0.0001559704],"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":[0.0004391027,0.0003570353,0.2191723,0.0003515888,0.0005164942,0.000716593,0.000800483,0.05237909,0.02814041,0.1808798,0.01532881,0.5009183],"study_design_scores_gemma":[0.00003746473,0.0002333465,0.460136,0.00006361968,0.0001188326,0.00102383,0.0002146476,0.3970781,0.01186013,0.103893,0.0251894,0.0001516122],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6483034,0.001460872,0.3103254,0.000731703,0.0001987554,0.00009674682,0.005301829,0.001288253,0.03229307],"genre_scores_gemma":[0.9722343,0.0005062812,0.02031667,0.00004635278,0.0003434377,0.00005448062,0.001691002,0.000139557,0.004667952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007482733,"threshold_uncertainty_score":0.02503222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01310742810970734,"score_gpt":0.1933683128068709,"score_spread":0.1802608846971636,"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."}}