{"id":"W2029484330","doi":"10.1190/1.2792998","title":"Nonlinear prediction and adaptive subtraction of curved events via Volterra series","year":2007,"lang":"en","type":"article","venue":"","topic":"Control Systems and Identification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Nonlinear system; Computer science; Algorithm; Deconvolution; Domain (mathematical analysis); Autoregressive model; Subtraction; Series (stratigraphy); Time series; Data mining; Artificial intelligence; Machine learning; Mathematics; Geology; Physics; Arithmetic; Mathematical analysis; Econometrics","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":[0.0001693541,0.0000488217,0.00007149188,0.00005002707,0.00001875835,0.000005410815,0.00001664264,0.00003984867,0.00001463606],"category_scores_gemma":[0.000005647019,0.00004744944,0.00001685439,0.00005440977,0.000007337166,0.0002307824,0.00000365147,0.00003586988,0.000004774212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000221175,"about_ca_system_score_gemma":0.000002043677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001413897,"about_ca_topic_score_gemma":0.00027924,"domain_scores_codex":[0.9996144,0.000006050925,0.0001794099,0.00006393599,0.00007074221,0.00006546484],"domain_scores_gemma":[0.9998264,0.00001073291,0.00003009963,0.00006560497,0.0000440145,0.00002317731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001989723,0.0000920225,0.01961639,0.0001915842,0.0001636223,0.000001230143,0.0008741699,0.001094835,0.9016333,0.000517592,0.0005916827,0.07502456],"study_design_scores_gemma":[0.0006273353,0.0001372366,0.6687332,0.00004334112,0.00003249009,0.00001778161,0.0004313143,0.2519631,0.07474515,0.0001327505,0.002979851,0.0001565084],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.767316,0.0001033888,0.2305454,0.00001063451,0.0004491773,0.0001476098,0.000007869861,0.0001041049,0.001315831],"genre_scores_gemma":[0.9991575,0.00002231176,0.0003475251,0.00000105895,0.00008883543,0.000003292376,0.00000984539,0.000007177312,0.0003623948],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8268882,"threshold_uncertainty_score":0.1934932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008525915372399096,"score_gpt":0.1992076115211683,"score_spread":0.1906816961487692,"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."}}