{"id":"W2116343256","doi":"10.1029/2009wr008353","title":"Bayesian data fusion for water table interpolation: Incorporating a hydrogeological conceptual model in kriging","year":2010,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Kriging; Hydrogeology; Interpolation (computer graphics); Water table; Aquifer; Geostatistics; Geology; Groundwater; Bayesian probability; Hydrology (agriculture); Algorithm; Data mining; Computer science; Statistics; Mathematics; Geotechnical engineering; Artificial intelligence; Spatial variability","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":[0.004111725,0.0006225568,0.001148689,0.001194517,0.0004569933,0.0009800134,0.001208607,0.0008424111,0.0005562323],"category_scores_gemma":[0.008958337,0.0007507084,0.001058826,0.001716058,0.0006372726,0.002063129,0.001404922,0.001285482,0.0002892789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00111472,"about_ca_system_score_gemma":0.001524198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02025625,"about_ca_topic_score_gemma":0.01824194,"domain_scores_codex":[0.9984661,0.0005959233,0.00009022504,0.0002180863,0.0005374785,0.0000921094],"domain_scores_gemma":[0.9981188,0.001139178,0.0001932443,0.0001897325,0.0003234879,0.00003549986],"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":[0.00009659841,0.0000744756,0.001747535,0.00007145778,0.00006280991,0.00003973539,0.0002278377,0.8891422,0.004183332,0.009826365,0.0002289769,0.09429883],"study_design_scores_gemma":[0.000005920725,0.00001622832,0.0004453424,0.00000872584,0.000008194261,0.00001079897,0.00001093877,0.9951763,0.0008694819,0.00315797,0.0002719878,0.00001809276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01230225,0.00006649907,0.9870675,0.00003438397,0.000005966901,0.00002225121,0.00003713652,0.0001908004,0.0002731709],"genre_scores_gemma":[0.4109942,0.0002385835,0.5878505,0.00006189657,0.00001412548,0.0001278041,0.0002678532,0.00009333038,0.0003517872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02025625,"threshold_uncertainty_score":0.04027671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08618725357369449,"score_gpt":0.3395785134309144,"score_spread":0.2533912598572199,"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."}}