{"id":"W6961831888","doi":"10.15456/jae.2022326.0709097886","title":"Improving Markov switching models using realized variance (replication data)","year":2018,"lang":"en","type":"other","venue":"ZBW Journal Data Archive","topic":"Wheat and Barley Genetics and Pathology","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Realized variance; Markov chain; Volatility (finance); Univariate; Factor analysis; Stochastic volatility; Markov model; Portfolio; Dimension (graph theory)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01232386,0.001303057,0.00197613,0.001480541,0.0006971539,0.002080478,0.002845972,0.0020421,0.003195],"category_scores_gemma":[0.03597876,0.001257235,0.002626065,0.001330667,0.001130335,0.003581536,0.002534257,0.00311649,0.0009423722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001313693,"about_ca_system_score_gemma":0.001680816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01166952,"about_ca_topic_score_gemma":0.008457151,"domain_scores_codex":[0.9960482,0.002322553,0.0001586449,0.0007718064,0.0004112581,0.0002874979],"domain_scores_gemma":[0.9704023,0.0237404,0.001787823,0.002719552,0.0009929292,0.0003569292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003217164,0.0001580562,0.007506406,0.0001171679,0.0003004235,0.0001632001,0.0003248879,0.8486233,0.001369333,0.06976425,0.001762916,0.06958836],"study_design_scores_gemma":[0.00001863597,0.00003048152,0.0003980966,0.000009215309,0.00002494508,0.00001749089,0.000007648259,0.9747525,0.0002192321,0.02411542,0.0003920491,0.0000143532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.04334851,0.0003740292,0.9533905,0.0004100357,0.00005589405,0.0000536566,0.0002835358,0.0008287197,0.001255107],"genre_scores_gemma":[0.7631881,0.0007130909,0.22741,0.0003267846,0.0002464889,0.0002803118,0.001838159,0.0004451927,0.005551966],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.01232386,"threshold_uncertainty_score":0.06517553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1011905003700452,"score_gpt":0.298895102736587,"score_spread":0.1977046023665417,"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."}}