{"id":"W4293256831","doi":"10.1007/s11222-022-10117-y","title":"A Joint estimation approach to sparse additive ordinary differential equations","year":2022,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Control Systems and Identification","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Science and Technology Commission of Shanghai Municipality; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Ode; Coordinate descent; Ordinary differential equation; Mathematics; Regularization (linguistics); Mathematical optimization; Applied mathematics; Convergence (economics); Algorithm; Computer science; Differential equation; Artificial intelligence","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":[0.00009106741,0.00006570943,0.00009449048,0.00006359746,0.0002493473,0.0000606005,0.00003728432,0.000009592096,0.00002566867],"category_scores_gemma":[0.00002709288,0.00007597334,0.00001231494,0.00009233029,0.000006161967,0.00002366895,0.00006235638,0.00007688052,0.000004995788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000466904,"about_ca_system_score_gemma":0.000007513297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003951044,"about_ca_topic_score_gemma":0.000002909623,"domain_scores_codex":[0.9994711,0.00002717392,0.0001682329,0.0001162152,0.0001120653,0.0001052006],"domain_scores_gemma":[0.9997768,0.00005142476,0.00003202526,0.00007191828,0.00002628313,0.00004157543],"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":[0.000007040055,0.00006721955,0.00002200211,0.0000842506,0.00005291892,0.000002900069,0.002474892,0.6668722,0.002048662,0.06842537,0.01072637,0.2492162],"study_design_scores_gemma":[0.0001302717,0.00002482616,0.003213683,0.000006512896,0.00001211367,0.000004116658,0.0002263903,0.995124,0.000009048906,0.0005791279,0.0005814751,0.00008844757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0289304,0.00004110889,0.9695499,0.00001206718,0.0002854346,0.0001873234,0.0002425703,0.00006544677,0.0006857369],"genre_scores_gemma":[0.9803494,0.000001076462,0.01923652,0.000009770265,0.00006557797,0.00003700368,0.00021519,0.00001151596,0.00007400028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9514189,"threshold_uncertainty_score":0.3098103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01711570270259815,"score_gpt":0.2163203003149313,"score_spread":0.1992045976123331,"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."}}