{"id":"W3014526472","doi":"10.5539/jmr.v12n2p52","title":"A Note on Stability of Stochastic Logistic Model by Incorporating the Ornstein-Uhlenbeck Process","year":2020,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Mathematics; Ornstein–Uhlenbeck process; Logistic function; Stochastic differential equation; Stability (learning theory); Applied mathematics; Lyapunov function; Zero (linguistics); Function (biology); Stochastic modelling; Stochastic process; Mathematical analysis; Statistics; Nonlinear system; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001296096,0.0007460411,0.0007746041,0.0004766731,0.0008188058,0.00113599,0.0007660351,0.001239108,0.002496297],"category_scores_gemma":[0.003385607,0.0002847565,0.001619313,0.0003179262,0.001238729,0.001872568,0.001698154,0.00158899,0.0002684475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005549616,"about_ca_system_score_gemma":0.001034567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00404061,"about_ca_topic_score_gemma":0.001790215,"domain_scores_codex":[0.9994542,0.0001931728,0.00003235399,0.0001283841,0.0001338406,0.00005806044],"domain_scores_gemma":[0.9993369,0.0002921436,0.0001136478,0.00006275831,0.0001419973,0.00005248285],"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":[0.00008073173,0.00003798629,0.002567288,0.0001806797,0.0001014743,0.000617823,0.000292761,0.3830537,0.01108668,0.5899637,0.001461817,0.01055529],"study_design_scores_gemma":[0.00001367923,0.00006825579,0.0003538812,0.00002222587,0.00002474594,0.0001132217,0.00003532874,0.9093066,0.0008112966,0.08718848,0.002036085,0.00002618837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09862726,0.001635024,0.874783,0.001641408,0.0003285673,0.00005415037,0.0001020644,0.0002037049,0.02262472],"genre_scores_gemma":[0.955395,0.00111444,0.03530337,0.0002035718,0.0001648311,0.0000801252,0.00007729427,0.0000716112,0.00758975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00404061,"threshold_uncertainty_score":0.008350968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2531829259719779,"score_gpt":0.3716378107505332,"score_spread":0.1184548847785553,"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."}}