{"id":"W3165303030","doi":"10.1016/j.advwatres.2021.103960","title":"Individual and joint inversion of head and flux data by geostatistical hydraulic tomography","year":2021,"lang":"en","type":"article","venue":"Advances in Water Resources","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Horizon 2020; Arctic Goose Joint Venture; Agence Nationale de la Recherche; European Commission","keywords":"Inversion (geology); Hydraulic conductivity; Borehole; Geology; Tomography; Hydraulic head; Flux (metallurgy); Soil science; Geotechnical engineering; Geomorphology; Structural basin; Optics; Physics; Materials science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001443331,0.00008039911,0.0001376633,0.00003096009,0.00006425974,0.00002478621,0.0001044135,0.00002395123,0.00009747131],"category_scores_gemma":[0.00001704721,0.00005822529,0.000008857322,0.00008225555,0.0003237574,0.0003378018,0.0007838362,0.00005478679,0.000006462926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000693745,"about_ca_system_score_gemma":8.116085e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001463105,"about_ca_topic_score_gemma":0.0003593238,"domain_scores_codex":[0.9991696,0.00005063836,0.0001628609,0.0002878482,0.0001845521,0.0001445419],"domain_scores_gemma":[0.9997295,0.00004039926,0.00002586834,0.0001606369,0.00000471151,0.00003885715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002770526,0.0001064011,0.7137129,0.00007540931,0.00002539081,0.00003268456,0.00832586,0.00001750102,0.01338459,0.00003704784,0.001383208,0.2628713],"study_design_scores_gemma":[0.001028389,0.0001415931,0.3162112,0.00007004776,0.0000346808,0.0000199536,0.001909298,0.0004186486,0.03785541,0.001430151,0.6405869,0.0002937556],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953045,0.003261163,0.000469665,0.0003327369,0.00002478296,0.00004931986,0.0000452477,0.000006515763,0.0005060566],"genre_scores_gemma":[0.9982249,0.0004117409,0.0008001252,0.0001567052,0.000006018548,0.000003583154,0.00009245268,0.000004002445,0.0003005157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6392037,"threshold_uncertainty_score":0.2374358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01639389439426482,"score_gpt":0.245099302176786,"score_spread":0.2287054077825212,"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."}}