{"id":"W2964211272","doi":"10.1016/j.spa.2019.01.003","title":"Nonlinear stochastic time-fractional slow and fast diffusion equations on <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\" overflow=\"scroll\" id=\"d1e219\" altimg=\"si9.gif\"><mml:msup><mml:mrow><mml:mi mathvariant=\"double-struck\">R</mml:mi></mml:mrow><mml:mrow><mml:mi>d</mml:mi></mml:mrow></mml:msup></mml:math>","year":2019,"lang":"lv","type":"article","venue":"Stochastic Processes and their Applications","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Army Research Office; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Uniqueness; Moment (physics); Nonlinear system; Mathematical analysis; Applied mathematics; Combinatorics","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","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005637341,0.0005889385,0.0003187435,0.0003676268,0.001657618,0.0008522485,0.0009754337,0.001027916,0.03081821],"category_scores_gemma":[0.0007205047,0.000997679,0.0005010307,0.0009896758,0.0007671831,0.0007112244,0.0008065968,0.0008732267,0.002012397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001639225,"about_ca_system_score_gemma":0.001008571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006557665,"about_ca_topic_score_gemma":0.0003113433,"domain_scores_codex":[0.995531,0.00003129537,0.001267415,0.001446979,0.0005922628,0.001131111],"domain_scores_gemma":[0.9952046,0.001297429,0.001472412,0.001278528,0.0001850229,0.0005619907],"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":[0.0005480092,0.0004142307,0.000003425821,0.0008521204,0.0004609582,0.00001877159,0.001054745,0.002778685,0.0003667462,0.9882215,0.002978851,0.00230196],"study_design_scores_gemma":[0.002801272,0.00149422,0.0001357783,0.000895692,0.0008741408,0.0005596109,0.001833425,0.946002,0.03425261,0.006576173,0.002870804,0.00170426],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2433015,0.002333427,0.591297,0.001582306,0.001195619,0.0002258865,0.00275471,0.0002017793,0.1571077],"genre_scores_gemma":[0.9908795,0.0004435189,0.002026268,0.000544443,0.001292806,0.002658796,0.00156431,0.0003063739,0.0002839914],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9816453,"threshold_uncertainty_score":0.9996421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01481594053370506,"score_gpt":0.2242869641313666,"score_spread":0.2094710235976616,"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."}}