{"id":"W3028724832","doi":"10.1103/physreve.102.022204","title":"Sparse identification of slow timescale dynamics","year":2020,"lang":"en","type":"article","venue":"Physical review. E","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Air Force Office of Scientific Research","keywords":"Computer science; Dynamic mode decomposition; Scale (ratio); Identification (biology); Dynamics (music); Tracking (education); Cluster analysis; Algorithm; Statistical physics; Artificial intelligence; Machine learning; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004801707,0.00008399704,0.0002360044,0.000006122857,0.00002304207,0.000007433472,0.0001103838,0.0000090685,0.0001642795],"category_scores_gemma":[0.000008108275,0.00007229637,0.0001588218,0.0001663299,0.00002861978,0.00007182658,0.00002892028,0.0001108839,0.0001859993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007848314,"about_ca_system_score_gemma":0.00001387372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004685731,"about_ca_topic_score_gemma":6.181173e-8,"domain_scores_codex":[0.9993547,0.00003550369,0.0002332993,0.0001601334,0.0001225139,0.00009386631],"domain_scores_gemma":[0.9995527,0.0000207644,0.0001411735,0.0001496232,0.00004235297,0.00009333179],"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":[0.00002992258,0.0006958758,0.001018818,0.0008454159,0.0001006277,7.862217e-7,0.0002550857,0.0009750578,0.02802914,0.2064927,0.04219003,0.7193666],"study_design_scores_gemma":[0.0004669769,0.0001328443,0.0005320674,0.0004954578,0.000258473,5.998195e-7,0.0000689907,0.8880807,0.03323387,0.02180289,0.05445262,0.0004745625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6518483,0.005946285,0.2123486,0.03753311,0.0007267753,0.002348143,0.0002697973,0.000245728,0.08873325],"genre_scores_gemma":[0.9986786,0.0003259758,0.00005686727,0.0003022063,0.0003824561,0.00001374347,0.00005163325,0.000009331348,0.0001792112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8871056,"threshold_uncertainty_score":0.2948161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02189984747583286,"score_gpt":0.3040371376675983,"score_spread":0.2821372901917654,"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."}}