{"id":"W4205978685","doi":"10.1007/978-0-8176-8394-8_10","title":"Fast Chaotic Artificial Time Integration","year":2012,"lang":"en","type":"book-chapter","venue":"Birkhäuser Boston eBooks","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gradient descent; Chaotic; Residual; Applied mathematics; Mathematics; Nonlinear system; Constant (computer programming); Regularization (linguistics); Method of steepest descent; Norm (philosophy); Algebraic number; Mathematical optimization; Computer science; Algorithm; Mathematical analysis; Artificial neural network; Artificial intelligence; Physics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001027418,0.0004889309,0.0004203691,0.0001434718,0.0001622836,0.00007947042,0.0001888431,0.0002755467,0.008782729],"category_scores_gemma":[9.837237e-7,0.000454743,0.0003107967,0.00001377461,0.0001054936,0.00008544376,0.00007548359,0.0006066675,0.003383541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004604615,"about_ca_system_score_gemma":0.00006297664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002211447,"about_ca_topic_score_gemma":0.000004269591,"domain_scores_codex":[0.9984542,0.00002410421,0.0004367623,0.0004246569,0.0002744321,0.0003858174],"domain_scores_gemma":[0.9989745,0.00002521633,0.0002821762,0.0004339736,0.00008325693,0.0002008547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000038919,0.00003311475,0.000005489781,0.000009336125,0.0001039039,0.000002530592,0.0002144655,0.0000133803,0.001172857,0.5408184,0.007691651,0.4498959],"study_design_scores_gemma":[0.0004225657,0.0001166679,0.00001611918,0.0003077347,0.000354867,0.00001241876,0.00004895296,0.001458356,0.005999298,0.0997159,0.8899561,0.001591044],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0005477567,0.0001041658,0.002880034,0.00009296495,0.0009621326,0.000457563,0.00008665983,0.0001474158,0.9947213],"genre_scores_gemma":[0.09560068,0.000005303048,0.0001073376,0.0001167383,0.003939675,0.00002958693,0.0003115644,0.0001371127,0.899752],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8822644,"threshold_uncertainty_score":0.9997904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02873766287141989,"score_gpt":0.2318289072188015,"score_spread":0.2030912443473816,"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."}}