{"id":"W2766402542","doi":"10.1063/1.5007866","title":"Mean, covariance, and effective dimension of stochastic distributed delay dynamics","year":2017,"lang":"en","type":"article","venue":"Chaos An Interdisciplinary Journal of Nonlinear Science","topic":"Nonlinear Dynamics and Pattern Formation","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Delay differential equation; Stochastic partial differential equation; Mathematics; Covariance; Stochastic differential equation; Dimension (graph theory); Applied mathematics; Eigenfunction; Distributed parameter system; Noise (video); Differential equation; Computation; Dynamical systems theory; Mathematical analysis; Computer science; Eigenvalues and eigenvectors; Algorithm; Physics","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":[0.001411005,0.0001607384,0.0003074626,0.0002684605,0.0008750539,0.0003726296,0.00191308,0.00005501552,0.00000220689],"category_scores_gemma":[0.0001080642,0.0001265231,0.00008178892,0.0002381059,0.0006935955,0.002880997,0.001593451,0.0002536431,0.000002575318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001077388,"about_ca_system_score_gemma":0.0001609025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007685135,"about_ca_topic_score_gemma":0.00002797887,"domain_scores_codex":[0.9983771,0.00004787933,0.000518191,0.0002857652,0.0005189192,0.0002521194],"domain_scores_gemma":[0.9972182,0.00007666761,0.001080542,0.0006946537,0.0007103827,0.0002195815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001690192,0.00370333,0.02017523,0.0006453546,0.0004298039,0.0009203183,0.04185145,0.04540541,0.05786638,0.126762,0.0001232318,0.7004272],"study_design_scores_gemma":[0.0005071904,0.0009243021,0.0208603,0.0002314403,0.00001499516,0.0004859425,0.0001708235,0.9728323,0.0006101372,0.003205938,0.00000505887,0.0001515931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5006397,0.00002176025,0.4984283,0.0002743663,0.0004732471,0.00009209274,0.0000296742,0.000006620095,0.0000342575],"genre_scores_gemma":[0.968098,0.000008169126,0.03175494,0.00001717294,0.0001003228,0.000001158755,0.000005810448,0.000007291646,0.000007082876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9274269,"threshold_uncertainty_score":0.6730295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01182804236380894,"score_gpt":0.3060100182184549,"score_spread":0.294181975854646,"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."}}