{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005538015,0.0002321029,0.0005484929,0.0002579956,0.0002736516,0.0001551366,0.0002615697,0.00008714374,0.001053299],"category_scores_gemma":[0.00007304126,0.0001970634,0.0003916804,0.00003346257,0.0001307906,0.0001258743,0.0004013116,0.0006908014,0.00002399165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005310484,"about_ca_system_score_gemma":0.00004147095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003551122,"about_ca_topic_score_gemma":0.000434996,"domain_scores_codex":[0.998633,0.00009612798,0.0005889817,0.0002255255,0.0002940085,0.0001624083],"domain_scores_gemma":[0.9989806,0.00003925175,0.0005516522,0.0001855466,0.0001226956,0.000120283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005379532,0.0001216428,0.0003788784,0.000460353,0.0003937033,0.0004977888,0.02619198,0.00005356107,3.662419e-7,0.03272521,0.8577942,0.08084431],"study_design_scores_gemma":[0.000280056,0.0001148446,0.002114868,0.0001517506,0.0008769156,0.000001738709,0.0009997011,0.00008747246,4.0795e-7,0.02029819,0.9748622,0.0002118826],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03344332,0.008693868,0.007026481,0.001889081,0.008835098,0.0006637892,0.0003971588,0.00008131722,0.9389699],"genre_scores_gemma":[0.9272392,0.01987585,0.002067507,0.001064058,0.00709279,0.00001814823,0.00003375319,0.00006008569,0.04254856],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8964213,"threshold_uncertainty_score":0.9998599,"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."}}