{"id":"W1978309254","doi":"10.1007/s00477-014-0855-x","title":"Uncertainty-based multi-criteria calibration of rainfall-runoff models: a comparative study","year":2014,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Markov chain Monte Carlo; Computer science; Calibration; GLUE; Bayesian probability; Metric (unit); Bayesian inference; Context (archaeology); Sampling (signal processing); Statistics; Econometrics; Mathematics; Artificial intelligence","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":[0.008786959,0.00086963,0.000857575,0.002097698,0.0004633143,0.00199336,0.001327395,0.001669381,0.000674599],"category_scores_gemma":[0.02140558,0.0004802004,0.00117566,0.001881075,0.0007715696,0.001747441,0.001129976,0.0007895228,0.00009932288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002056878,"about_ca_system_score_gemma":0.001115891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01315135,"about_ca_topic_score_gemma":0.004648915,"domain_scores_codex":[0.9971707,0.001715648,0.0001231941,0.0002419267,0.0006084125,0.000140217],"domain_scores_gemma":[0.9846691,0.01203708,0.0008267649,0.0008556254,0.001459735,0.0001516893],"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":[0.0001208404,0.00004965662,0.002583567,0.0000426295,0.00006761271,0.00002364787,0.00003770859,0.9840248,0.0003441098,0.00123572,0.00009393667,0.01137575],"study_design_scores_gemma":[0.000009127672,0.00003993307,0.001650854,0.00001186463,0.00001789093,0.00001216431,0.00001823456,0.9965249,0.0004824903,0.001087724,0.0001315922,0.00001322368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8110254,0.002286817,0.1785378,0.0007217479,0.00005459306,0.00007274152,0.0003727025,0.000342276,0.0065859],"genre_scores_gemma":[0.9910339,0.0002556608,0.008323756,0.00002250705,0.00000854619,0.00001386972,0.0001152198,0.00004173249,0.0001848957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01315135,"threshold_uncertainty_score":0.04647046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0592669652570973,"score_gpt":0.3627842250932972,"score_spread":0.3035172598361999,"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."}}