{"id":"W1978429862","doi":"10.1088/0266-5611/23/3/025","title":"Dynamic level set regularization for large distributed parameter estimation problems","year":2007,"lang":"en","type":"article","venue":"Inverse Problems","topic":"Numerical methods in inverse problems","field":"Mathematics","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mathematics; Inverse problem; Conjugate gradient method; Regularization (linguistics); Applied mathematics; Linear system; Iterative method; Piecewise linear function; Mathematical optimization; Scaling; Tikhonov regularization; Algorithm; Computer science; Mathematical analysis","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":[0.001874598,0.0007648136,0.001114142,0.0006333662,0.0005744669,0.001033087,0.0009355253,0.001677371,0.001060853],"category_scores_gemma":[0.005009395,0.0005260051,0.000760701,0.0005156187,0.001693513,0.001100501,0.00222805,0.001973716,0.0003766163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007998141,"about_ca_system_score_gemma":0.001005493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001596007,"about_ca_topic_score_gemma":0.001202738,"domain_scores_codex":[0.9992378,0.0003368027,0.00002605449,0.00009472617,0.0002669741,0.0000376307],"domain_scores_gemma":[0.9982292,0.001316321,0.0001197856,0.0001284919,0.0001394774,0.00006666484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004389715,0.00003288802,0.0003526826,0.000111598,0.0000469746,0.0001500884,0.0001347095,0.8212394,0.006947578,0.1322702,0.001032106,0.03763791],"study_design_scores_gemma":[0.000004304758,0.000006219002,0.00002554761,0.000003904261,0.000002184814,0.00001316764,0.0000055651,0.9803419,0.0005112124,0.01827517,0.0008069437,0.000003933832],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003980922,0.0001996822,0.9945134,0.0001892979,0.00001345465,0.00001280699,0.000007073935,0.00006343505,0.001019898],"genre_scores_gemma":[0.319244,0.0008820918,0.6730576,0.0001985427,0.0001200864,0.0003575683,0.0001539122,0.0002348091,0.005751431],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001874598,"threshold_uncertainty_score":0.009913921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1166349679503158,"score_gpt":0.3697990849703669,"score_spread":0.253164117020051,"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."}}