{"id":"W3111877870","doi":"10.1016/j.jhydrol.2020.125874","title":"On the importance of considering specific storage heterogeneity in hydraulic tomography: Laboratory sandbox and synthetic studies","year":2020,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Beijing Municipal Natural Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Foundation for Innovation; Ontario Research Foundation","keywords":"Geostatistics; Hydraulic conductivity; Spatial heterogeneity; Soil science; Spatial variability; Geology; Reliability (semiconductor); Variogram; Statistics; Mathematics; Kriging; Thermodynamics","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":[0.000334483,0.00008002883,0.0002489566,0.00003936196,0.00004712892,0.000005162713,0.00009960429,0.00002450946,0.00005184132],"category_scores_gemma":[0.00007956056,0.00005128106,0.00004109261,0.0001322274,0.000318694,0.00006835289,0.000081181,0.0001379878,0.000004079307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000259134,"about_ca_system_score_gemma":0.000004505474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002936244,"about_ca_topic_score_gemma":0.00004709226,"domain_scores_codex":[0.9992265,0.0001164362,0.0003002117,0.0001110165,0.0001394786,0.0001063821],"domain_scores_gemma":[0.9994537,0.0002102697,0.0002075878,0.00007630626,0.00001589664,0.00003630205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002067178,0.00009614992,0.9567764,0.00002701551,0.0001816215,0.0002639451,0.005923535,0.001937834,0.03130015,0.0003613062,0.001477706,0.001447571],"study_design_scores_gemma":[0.002464211,0.002273778,0.9428087,0.0001115026,0.00009652151,0.0001956066,0.003788275,0.0008405559,0.02645845,0.003359752,0.01718007,0.0004226144],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932959,0.002827444,0.00007714248,0.003626969,0.00006280911,0.00005403609,0.000001428647,0.000002522323,0.00005172686],"genre_scores_gemma":[0.9985185,0.0003153471,0.0000348716,0.001104642,0.00001846107,0.000001990412,4.376147e-8,0.000003863983,0.000002333693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01570236,"threshold_uncertainty_score":0.2091181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02637940736820002,"score_gpt":0.2433028348268306,"score_spread":0.2169234274586306,"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."}}