{"id":"W2609549470","doi":"10.3934/dcdsb.2017144","title":"Predicting and estimating probability density functions of chaotic systems","year":2017,"lang":"en","type":"article","venue":"Discrete and Continuous Dynamical Systems - B","topic":"Marine and environmental studies","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Chaotic; Probability density function; Mathematics; Measure (data warehouse); Probability measure; Lebesgue measure; Invariant (physics); Limiting; Statistical physics; Lebesgue integration; Invariant measure; Chaotic hysteresis; Function (biology); Absolute continuity; Sequence (biology); Applied mathematics; Mathematical analysis; Synchronization of chaos; Statistics; Computer science; Control theory (sociology); Physics; Ergodic theory; Artificial intelligence","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":[0.001447318,0.0004478425,0.0005171531,0.001593602,0.0002631096,0.0007313598,0.0005361752,0.0006952427,0.0003792014],"category_scores_gemma":[0.007747849,0.0002828656,0.0003597476,0.0005700655,0.0005597261,0.001030315,0.0006359023,0.0005710084,0.0001257672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005136619,"about_ca_system_score_gemma":0.0004202017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00299998,"about_ca_topic_score_gemma":0.001143254,"domain_scores_codex":[0.9997529,0.00009762378,0.00001215072,0.00005432079,0.00005137342,0.00003163473],"domain_scores_gemma":[0.9969904,0.00235358,0.0002783708,0.0001055922,0.0002073065,0.0000647409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000482184,0.0000365696,0.01721044,0.00007116194,0.00004999606,0.0001431957,0.0001456713,0.921071,0.004698968,0.01256677,0.0003442652,0.0436138],"study_design_scores_gemma":[7.40557e-7,0.000003651596,0.0007712617,0.000002332813,0.00000175025,0.00001391452,0.000005958427,0.996655,0.0005830131,0.001893873,0.0000626765,0.000005848957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2888139,0.0003356247,0.7094609,0.0001084797,0.00001418828,0.00002863257,0.00008013234,0.0003084077,0.0008497204],"genre_scores_gemma":[0.934944,0.0001636636,0.06419241,0.00001356665,0.00001575925,0.00003189297,0.0001093689,0.00002907789,0.0005004432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00299998,"threshold_uncertainty_score":0.00765425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01062455078965671,"score_gpt":0.1966312083571348,"score_spread":0.1860066575674781,"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."}}