{"id":"W4293510464","doi":"10.1175/jhm-d-21-0158.1","title":"A Mixed-Level Factorial Inference Approach for Ensemble Long-Term Hydrological Projections over the Jing River Basin","year":2022,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Streamflow; Forcing (mathematics); Precipitation; Drainage basin; Representative Concentration Pathways; Climate change; Term (time); Climatology; Hydrological modelling; Flooding (psychology); Climate model; Hydrology (agriculture); Meteorology; Geology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003081944,0.0006604569,0.000864909,0.0009527903,0.0006954448,0.0008482537,0.001537413,0.0008452394,0.001676221],"category_scores_gemma":[0.005119647,0.000690372,0.001391427,0.0007530887,0.0004157483,0.0007616593,0.0007516114,0.0008553161,0.0001488755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000864287,"about_ca_system_score_gemma":0.001476547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03693344,"about_ca_topic_score_gemma":0.03719574,"domain_scores_codex":[0.9994107,0.0002812392,0.00003459428,0.0001377877,0.00007942194,0.00005621566],"domain_scores_gemma":[0.9981249,0.00121156,0.0001927579,0.00009486625,0.0002837141,0.00009224896],"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":[0.00006957523,0.00004135564,0.003823489,0.00002638879,0.0001392584,0.00005483985,0.0000374016,0.9730073,0.0008338702,0.00249903,0.0002533676,0.01921419],"study_design_scores_gemma":[0.000002101372,0.00000336956,0.0002144472,9.624267e-7,0.000004557488,7.86867e-7,0.000001483706,0.9993876,0.00003844958,0.0003196628,0.00002395562,0.000002565576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2018751,0.0002900697,0.7953765,0.0002641938,0.00004419756,0.00006266698,0.0004984458,0.0005061105,0.001082776],"genre_scores_gemma":[0.878075,0.0001286886,0.1197627,0.00008220087,0.00007479108,0.0001630574,0.00100256,0.00006376285,0.0006471481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03693344,"threshold_uncertainty_score":0.07343692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05104239155358179,"score_gpt":0.2702990523456801,"score_spread":0.2192566607920983,"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."}}