{"id":"W3122438089","doi":"10.1142/s0219024901001140","title":"FINANCIAL SIGNAL PROCESSING: A SELF CALIBRATING MODEL","year":2001,"lang":"en","type":"preprint","venue":"International Journal of Theoretical and Applied Finance","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Markov chain; Computer science; Econometrics; Treasury; Markov model; Bond; Markov process; Short rate; Discretization; SIGNAL (programming language); Hidden Markov model; Cox–Ingersoll–Ross model; Interest rate; Finance; Economics; Mathematics; Yield curve; Statistics; Artificial intelligence; Machine learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004600657,0.0003039643,0.0006865684,0.0002164293,0.0001086618,0.000215671,0.0008798805,0.0003437283,0.00005145153],"category_scores_gemma":[0.0001080997,0.000307315,0.0001972744,0.0001393307,0.0003167273,0.0001304839,0.0004796621,0.0008649145,0.0000187678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001083665,"about_ca_system_score_gemma":0.0002554212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004866932,"about_ca_topic_score_gemma":5.574471e-7,"domain_scores_codex":[0.9977896,0.0000041731,0.001266008,0.0004731896,0.0001837445,0.0002832704],"domain_scores_gemma":[0.9979811,0.00006682837,0.001417855,0.000170577,0.0002712782,0.00009237079],"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":[0.00010871,0.0001792392,0.0000520604,0.00005028839,0.00004480648,0.00001378957,0.0003263294,0.003001466,0.00001687312,0.9845195,0.0001626791,0.0115242],"study_design_scores_gemma":[0.00047418,0.00004138114,0.0001435959,0.0001532748,0.00002191146,0.00005575985,0.00001209222,0.0974144,0.00005976201,0.8985731,0.002749058,0.0003015084],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02238918,0.002125053,0.9624178,0.002423927,0.000379245,0.0002047347,0.0002211063,0.0000315744,0.009807423],"genre_scores_gemma":[0.9564486,0.0006183932,0.04124915,0.0005664926,0.0009674362,0.00004935381,0.00001274323,0.00003304413,0.00005474206],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9340594,"threshold_uncertainty_score":0.9999379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01562707028736999,"score_gpt":0.2355947193234635,"score_spread":0.2199676490360935,"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."}}