{"id":"W2594334996","doi":"10.1002/2016wr019185","title":"Incorporating geologic information into hydraulic tomography: A general framework based on geostatistical approach","year":2017,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Aquifer; Geology; Borehole; Aquifer properties; Geostatistics; Covariance; Hydraulic conductivity; Spatial analysis; Bayesian probability; Spatial variability; Soil science; Hydrology (agriculture); Groundwater; Geotechnical engineering; Statistics; Groundwater recharge; Remote sensing; Mathematics","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001365327,0.0001604339,0.0001641606,0.0001804191,0.002009901,0.0006464946,0.0007209625,0.0001182268,0.0003330403],"category_scores_gemma":[0.0002705872,0.0001047095,0.00005636326,0.000140504,0.0007766008,0.0004717815,0.0008063784,0.0004817529,0.001063084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001118781,"about_ca_system_score_gemma":0.000005696907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002050121,"about_ca_topic_score_gemma":0.00005557289,"domain_scores_codex":[0.9975019,0.0002686865,0.00025138,0.0003331698,0.00108399,0.0005609051],"domain_scores_gemma":[0.9990003,0.0001187242,0.00006950556,0.0006291458,0.00005028766,0.0001320474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004668045,0.0004647021,0.7137059,0.0001506947,0.00006666746,0.00007154144,0.02486211,0.01098201,0.001746154,0.002835417,0.003716057,0.2409319],"study_design_scores_gemma":[0.001073532,0.0007490239,0.6056362,0.00006197786,0.00001427714,0.000005879011,0.001036644,0.2197081,0.002965864,0.01369977,0.1544107,0.0006380569],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9286857,0.000006763405,0.04342972,0.001631274,0.00005579031,0.0003564015,0.000004968106,0.00005072662,0.02577862],"genre_scores_gemma":[0.9899668,0.000001316268,0.008380514,0.0003863501,0.00007976562,0.0001475298,0.00004153619,0.00001019018,0.000986042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2402939,"threshold_uncertainty_score":0.9997147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03410128050065935,"score_gpt":0.3059517082762063,"score_spread":0.271850427775547,"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."}}