{"id":"W2087723158","doi":"10.1016/j.jconhyd.2009.01.003","title":"Fuzzy-stochastic characterization of site uncertainty and variability in groundwater flow and contaminant transport through a heterogeneous aquifer","year":2009,"lang":"en","type":"article","venue":"Journal of Contaminant Hydrology","topic":"Fuzzy Systems and Optimization","field":"Mathematics","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Geostatistics; Groundwater flow; Fuzzy logic; Aquifer; Groundwater; Fuzzy set; Uncertainty analysis; Uncertainty quantification; Flow (mathematics); Environmental science; Set (abstract data type); Hydrology (agriculture); Computer science; Spatial variability; Mathematical optimization; Mathematics; Geology; Statistics; Geotechnical engineering","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":[0.001071633,0.0001844177,0.0007731506,0.0001338986,0.00003844512,0.00001144751,0.00007844142,0.000187197,0.00001074096],"category_scores_gemma":[0.0001270202,0.0001405596,0.00007369683,0.00009420534,0.0001139193,0.0002386254,0.00001374776,0.0001745133,2.456664e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005091412,"about_ca_system_score_gemma":0.000045092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003826768,"about_ca_topic_score_gemma":0.0001148475,"domain_scores_codex":[0.9981328,0.0002420168,0.001040214,0.0001927953,0.0001686043,0.0002236108],"domain_scores_gemma":[0.9987767,0.0002165741,0.0006293575,0.0001480116,0.0001657884,0.00006356068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01691256,0.008218529,0.1763125,0.003964864,0.001315615,0.003093792,0.1048339,0.03961371,0.5409329,0.05170448,0.0000863247,0.05301081],"study_design_scores_gemma":[0.02262737,0.01578228,0.5667555,0.001989511,0.001388595,0.0102549,0.000453045,0.2312386,0.004265474,0.1427841,0.0007122399,0.00174839],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.94297,0.0001059157,0.05610355,0.0003207066,0.0001503828,0.0002943727,0.00001391637,0.000005708772,0.00003538556],"genre_scores_gemma":[0.9985297,0.0001000872,0.001157845,0.00009900496,0.00006337649,0.00000350454,0.000009517476,0.00001232829,0.00002458635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5366675,"threshold_uncertainty_score":0.5731855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01258435306023366,"score_gpt":0.2469371922452233,"score_spread":0.2343528391849897,"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."}}