{"id":"W2979713501","doi":"10.5194/hess-24-4601-2020","title":"An uncertainty partition approach for inferring interactive hydrologic risks","year":2020,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Copula (linguistics); Multivariate statistics; Econometrics; Computer science; Inference; Uncertainty analysis; Statistics; Factorial; Flood myth; Mathematics; Artificial intelligence; Geography","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":[0.0008230614,0.0001504478,0.0002694975,0.00004393081,0.0006557596,0.00004237976,0.0002616888,0.000149549,0.0001768983],"category_scores_gemma":[0.00004856386,0.0001169892,0.00006223764,0.0002915852,0.0008080686,0.0004448107,0.00008405251,0.0001369851,0.00007747432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001432328,"about_ca_system_score_gemma":0.00001108475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002608764,"about_ca_topic_score_gemma":0.00009073522,"domain_scores_codex":[0.9983628,0.000254032,0.0002351888,0.0006401703,0.0001418209,0.000366004],"domain_scores_gemma":[0.9994451,0.0001172462,0.0001260491,0.0001264416,0.000007820501,0.0001773377],"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":[0.0002493856,0.00008896661,0.28434,0.00004797597,0.00005646481,0.00001012288,0.002599456,0.7057247,0.002176519,0.002129309,0.0001183246,0.00245884],"study_design_scores_gemma":[0.00027036,0.000754348,0.005518126,0.000003186677,0.00004379917,0.00002557734,0.0006728239,0.9914819,0.0001952384,0.0003939118,0.0004882966,0.0001524397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9696093,0.00005249122,0.01889292,0.0005911842,0.00006436956,0.000259848,0.000006677959,0.00009317105,0.01042999],"genre_scores_gemma":[0.9972361,0.000005863225,0.001583679,0.001002695,0.00007979616,0.00006022476,0.00001363216,0.000004460285,0.00001348968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2857572,"threshold_uncertainty_score":0.5043639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04788105084223327,"score_gpt":0.2834139496011581,"score_spread":0.2355328987589248,"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."}}