{"id":"W3096595861","doi":"10.1063/5.0009326","title":"Learning dynamical systems in noise using convolutional neural networks","year":2020,"lang":"en","type":"article","venue":"Chaos An Interdisciplinary Journal of Nonlinear Science","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Chaotic; Computer science; Spectrogram; Artificial intelligence; Convolutional neural network; Noise (video); Dynamical systems theory; Pattern recognition (psychology); Image (mathematics)","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":[0.0004219076,0.0005537613,0.0004257401,0.000386327,0.0002251457,0.0006038216,0.0005322032,0.0005525115,0.0005201907],"category_scores_gemma":[0.001934446,0.0003226585,0.0004423743,0.0003433938,0.000465418,0.0008051501,0.0006265912,0.0008258322,0.0001288661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007215099,"about_ca_system_score_gemma":0.0005143733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00749317,"about_ca_topic_score_gemma":0.006822017,"domain_scores_codex":[0.9998612,0.00002841755,0.000009244746,0.00004369373,0.00003460598,0.00002274818],"domain_scores_gemma":[0.99949,0.0003084588,0.00008895557,0.0000354744,0.00005608652,0.00002087221],"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":[0.00003823113,0.00001845178,0.001326696,0.00003068672,0.00002894028,0.00004960042,0.0000328948,0.9507905,0.004376523,0.005905652,0.0002477941,0.03715403],"study_design_scores_gemma":[5.356986e-7,0.00000278177,0.00008303158,0.000001051836,0.000001097747,0.000002136699,0.000001093595,0.9985776,0.0002352199,0.001047622,0.00004683774,9.193177e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1444568,0.0007307443,0.8517858,0.0004026809,0.00005168765,0.00002372313,0.0001010207,0.0006585018,0.001788999],"genre_scores_gemma":[0.9294212,0.0004455846,0.06777111,0.00007979442,0.00005341172,0.00003969915,0.0002011923,0.00003944983,0.001948567],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00749317,"threshold_uncertainty_score":0.01489913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03050562928005968,"score_gpt":0.301102731013272,"score_spread":0.2705971017332123,"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."}}