{"id":"W2468085853","doi":"10.48550/arxiv.1607.00682","title":"Large time asymptotics for the parabolic Anderson model driven by space and time correlated noise","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Army Research Office; Division of Mathematical Sciences; National Science Foundation","keywords":"Mathematics; Brownian motion; Fractional Brownian motion; Dimension (graph theory); Covariance; Mathematical analysis; Hurst exponent; Heat kernel; Lyapunov exponent; Kernel (algebra); Gaussian noise; Gaussian; Noise (video); Mathematical physics; Statistical physics; Applied mathematics; Pure mathematics; Physics; Nonlinear system; Statistics; Quantum mechanics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001523721,0.001021305,0.0009442586,0.001265712,0.0008597932,0.001867786,0.001639976,0.002080561,0.002440587],"category_scores_gemma":[0.006653556,0.0005057368,0.0009322704,0.0005972722,0.002861696,0.002350427,0.002249345,0.001857091,0.0002378731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001746913,"about_ca_system_score_gemma":0.001452855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007867742,"about_ca_topic_score_gemma":0.003692458,"domain_scores_codex":[0.9996777,0.0001133762,0.00001307175,0.00004582456,0.00008262002,0.00006746929],"domain_scores_gemma":[0.9977593,0.0009906066,0.0004572915,0.00009093447,0.0002897201,0.0004120228],"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":[0.00007857459,0.00005549108,0.002050887,0.0001022325,0.00008821768,0.0005522877,0.0002886016,0.2523488,0.005038918,0.7356257,0.001675222,0.002094924],"study_design_scores_gemma":[0.00001395303,0.00001526426,0.0003602662,0.00001147623,0.00001168148,0.00004140018,0.000046163,0.9152026,0.0001753577,0.08382819,0.0002756957,0.00001784338],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6385966,0.002450935,0.3272769,0.00731073,0.0003010127,0.00007342592,0.0002325025,0.0004037657,0.02335409],"genre_scores_gemma":[0.9837599,0.0006703174,0.00608343,0.0002361828,0.0001377343,0.00006128434,0.0001116846,0.0000716606,0.008867955],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007867742,"threshold_uncertainty_score":0.01564389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03807418785277091,"score_gpt":0.1692366591750672,"score_spread":0.1311624713222962,"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."}}