{"id":"W1910751443","doi":"10.1002/hyp.9499","title":"Finding the most appropriate precipitation probability distribution for stochastic weather generation and hydrological modelling in Nordic watersheds","year":2012,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"National Natural Science Foundation of China","keywords":"Weibull distribution; Gamma distribution; Precipitation; Environmental science; Generalized Pareto distribution; Probability distribution; Natural exponential family; Statistics; Exponential distribution; Probability density function; Series (stratigraphy); Exponential function; Distribution fitting; Stochastic modelling; Extreme value theory; Meteorology; Mathematics; Geography; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003333513,0.0003516921,0.0005096442,0.0007896405,0.0004981143,0.001367843,0.0007061037,0.0006680873,0.0002431684],"category_scores_gemma":[0.006605029,0.0003186587,0.0003721627,0.001094672,0.0006424424,0.0007587514,0.0003947873,0.0004057292,0.00003652337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00303965,"about_ca_system_score_gemma":0.00273725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1275018,"about_ca_topic_score_gemma":0.09959274,"domain_scores_codex":[0.9994616,0.0003342482,0.00002826441,0.00006346193,0.00005881888,0.00005361458],"domain_scores_gemma":[0.9974338,0.001922394,0.000247148,0.00009123648,0.0002263726,0.00007892803],"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":[0.00004083919,0.00003073832,0.009941633,0.00002053361,0.00001923194,0.00006468501,0.00004890818,0.982152,0.0004305897,0.001595822,0.000108373,0.005546711],"study_design_scores_gemma":[0.00001224924,0.00001680028,0.004280102,0.00001014838,0.000007082514,0.00001018394,0.00005476887,0.9936923,0.0005258475,0.001238105,0.000142651,0.00000974758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9379467,0.0001411302,0.06061479,0.0001755479,0.000005610793,0.0000904647,0.000222559,0.0001067825,0.0006964828],"genre_scores_gemma":[0.9796552,0.00008493244,0.01988517,0.000009864401,0.000003057324,0.00003814553,0.0001524272,0.000009102153,0.0001621976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1275018,"threshold_uncertainty_score":0.2535193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04723295269555348,"score_gpt":0.2488098429248189,"score_spread":0.2015768902292654,"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."}}