{"id":"W1494789472","doi":"10.1155/2015/371272","title":"Ultrahigh Frequency Data Liquidity Duration Estimation: A Case Study of Chinese A Shares","year":2015,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Market liquidity; Market microstructure; Liquidity risk; Business; Duration (music); Econometrics; Accounting liquidity; Liquidity crisis; Financial economics; Economics; Order (exchange); Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007587741,0.0001329707,0.0003375574,0.0001444085,0.00002113116,0.00004356432,0.0002242084,0.00005498357,0.00005025645],"category_scores_gemma":[0.0008566541,0.0001274317,0.00002057398,0.0002571608,0.00001920395,0.0005200511,0.00009023252,0.0001117454,0.00001910865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004600128,"about_ca_system_score_gemma":0.00001517998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004056879,"about_ca_topic_score_gemma":0.00005866815,"domain_scores_codex":[0.9987407,0.000009633671,0.0007878712,0.0002520123,0.00005560557,0.0001542137],"domain_scores_gemma":[0.9992491,0.00006901012,0.0001585943,0.0004438185,0.0000232879,0.00005618595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002292686,0.003968753,0.06702007,0.00308656,0.0001420529,0.0005155373,0.0258526,0.3945561,0.0001174001,0.5039744,0.0003723071,0.0003712888],"study_design_scores_gemma":[0.0008342962,0.0002853055,0.002121209,0.0001135209,0.000006018262,0.0001064023,0.0004436244,0.7753552,0.00000554687,0.2204291,0.00003648792,0.0002632497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9384679,0.000318874,0.05831726,0.00004520854,0.0001212581,0.0004796026,0.00004241452,0.0000472429,0.002160295],"genre_scores_gemma":[0.9937822,0.000006024428,0.006078494,0.000004787951,0.00003143439,0.00005458953,0.00001574913,0.00001525633,0.00001152221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3807991,"threshold_uncertainty_score":0.5196514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0906000876347417,"score_gpt":0.2665199872519111,"score_spread":0.1759198996171694,"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."}}