{"id":"W2593571664","doi":"10.1142/s0219024917500194","title":"GENERAL SEMI-MARKOV MODEL FOR LIMIT ORDER BOOKS","year":2017,"lang":"en","type":"article","venue":"International Journal of Theoretical and Applied Finance","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Limit (mathematics); Markov chain; Computer science; Order (exchange); Implementation; Markov model; Mathematical economics; Applied mathematics; Mathematics; Economics; Programming language; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00036029,0.0001052562,0.0003488968,0.00007726016,0.0001551878,0.0002089494,0.0005176577,0.00005811478,0.000118936],"category_scores_gemma":[0.0001218379,0.00009550724,0.0001516301,0.00001873284,0.0002223884,0.0001277339,0.0001117179,0.0001080188,0.00001014574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002933248,"about_ca_system_score_gemma":0.00001668589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001044724,"about_ca_topic_score_gemma":0.000002808719,"domain_scores_codex":[0.999067,0.000002787711,0.0005429202,0.0001767236,0.00006698952,0.0001435821],"domain_scores_gemma":[0.9988878,0.00004412469,0.0006722988,0.0001857743,0.0001566,0.00005336242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001317552,0.00003137813,0.0003719144,0.000005670893,0.0001099638,0.000002492388,0.00005194493,0.0008792119,0.00004084321,0.9924254,0.0006146911,0.005334768],"study_design_scores_gemma":[0.0008596757,0.00004430484,0.001218385,0.00002340327,0.00001495632,0.00001955633,0.000009845696,0.2706268,0.0001021435,0.7008629,0.02606129,0.0001567918],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3406481,0.00147325,0.5525179,0.01149684,0.001317068,0.0002828374,0.0003676378,0.00001373956,0.09188259],"genre_scores_gemma":[0.9862398,0.0002245487,0.01066113,0.0002426959,0.0004053973,0.000008319507,0.000002529864,0.00001190204,0.002203708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6455916,"threshold_uncertainty_score":0.3894672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01953692995865189,"score_gpt":0.2401337950582508,"score_spread":0.2205968650995989,"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."}}