{"id":"W2126796876","doi":"10.1002/2014wr016552","title":"Should hydraulic tomography data be interpreted using geostatistical inverse modeling? A laboratory sandbox investigation","year":2015,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Strategic Environmental Research and Development Program; China Scholarship Council; Ontario Research Foundation","keywords":"Geostatistics; Geology; Calibration; Kriging; Data set; Synthetic data; Variogram; Inverse problem; Soil science; Computer science; Algorithm; Machine learning; Artificial intelligence; Statistics; Mathematics; Spatial variability","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":[0.002030116,0.0001668655,0.0001742453,0.0002188959,0.0003310731,0.0002104344,0.0007548899,0.00009660827,0.0002352833],"category_scores_gemma":[0.0002071144,0.0001220782,0.00002732003,0.0005580756,0.0007112277,0.0005534578,0.002056219,0.0003760319,0.0003021433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001965918,"about_ca_system_score_gemma":0.00002526011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003509324,"about_ca_topic_score_gemma":0.000504524,"domain_scores_codex":[0.9966803,0.0005331569,0.0002859976,0.0005973286,0.001309396,0.000593889],"domain_scores_gemma":[0.9987598,0.00006675629,0.00002588712,0.000688157,0.0001223019,0.0003371062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001248263,0.0006453034,0.3877372,0.0002265596,0.0004715782,0.0004539756,0.2730683,0.03408268,0.1393063,0.0001678485,0.1407164,0.02187547],"study_design_scores_gemma":[0.0006228633,0.0001270693,0.0007945024,0.00003386725,0.00002261559,0.00000613877,0.002890481,0.8767563,0.002690897,0.0007311848,0.1150324,0.0002917369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902979,0.00006765966,0.007617007,0.0009188546,0.00006483003,0.0002491726,0.00006012867,0.00005887694,0.0006655962],"genre_scores_gemma":[0.99762,0.000004935961,0.001192054,0.0004779131,0.0000694561,0.00002568431,0.0001351381,0.00002430344,0.0004505153],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8426736,"threshold_uncertainty_score":0.5305071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2408899868390142,"score_gpt":0.3682958662385027,"score_spread":0.1274058793994885,"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."}}