{"id":"W2790463476","doi":"10.1002/2017wr021884","title":"A Reduced‐Order Successive Linear Estimator for Geostatistical Inversion and its Application in Hydraulic Tomography","year":2018,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Recruitment Program of Global Experts; Ministry of Land and Resources of the People's Republic of China; National Natural Science Foundation of China; Citrus Research and Development Foundation","keywords":"Covariance; Estimator; Mathematics; Algorithm; Inverse problem; Eigenvalues and eigenvectors; Applied mathematics; Covariance function; Mathematical optimization; Discretization; Covariance matrix; Eigenfunction; Inverse; Statistics; Mathematical analysis; Geometry","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":[0.001221347,0.0004492298,0.0004387201,0.0005369725,0.0002251412,0.0004111314,0.0007600558,0.0005621671,0.001172731],"category_scores_gemma":[0.003549551,0.0003142779,0.0006128743,0.0005939663,0.0004486627,0.0005170672,0.0005736087,0.0006819701,0.0004198546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003307617,"about_ca_system_score_gemma":0.00121515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005707179,"about_ca_topic_score_gemma":0.005059204,"domain_scores_codex":[0.9995372,0.0001811349,0.00001917682,0.00006256661,0.0001732294,0.00002668254],"domain_scores_gemma":[0.9984208,0.001035093,0.0001256681,0.00009798455,0.0002908627,0.00002949395],"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":[0.0001024133,0.0001138567,0.001893451,0.0001464055,0.00009142104,0.0001135299,0.0001336116,0.7703977,0.03109591,0.01557455,0.001348992,0.1789882],"study_design_scores_gemma":[0.00000296273,0.00001140567,0.00009897062,0.000001934868,0.000002519461,0.000009286923,0.00000256767,0.9978294,0.001291202,0.0004807968,0.0002651045,0.000003917893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006401007,0.0000486627,0.9930668,0.00002962382,0.000007382494,0.00001397718,0.00001487651,0.00018484,0.0002327902],"genre_scores_gemma":[0.2038154,0.0002053385,0.7938718,0.0000528662,0.00003296353,0.0001541628,0.0001812002,0.0001031498,0.001583081],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005707179,"threshold_uncertainty_score":0.01134789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02859215899748105,"score_gpt":0.3239063905619206,"score_spread":0.2953142315644395,"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."}}