{"id":"W2760639860","doi":"10.1002/hyp.11368","title":"Using raw regional climate model outputs for quantifying climate change impacts on hydrology","year":2017,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":30,"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":"Environmental science; Climate change; Climate model; Climatology; Watershed; Uncertainty analysis; Hydrological modelling; Hydrology (agriculture); Meteorology; Computer science; Statistics; Mathematics; Geology; Geography","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":[0.002698339,0.0006406051,0.0003649655,0.001507641,0.0001720558,0.0007880586,0.0004305076,0.00043561,0.0007686324],"category_scores_gemma":[0.005932565,0.0001945965,0.0008153209,0.002325331,0.00026162,0.0007785852,0.0004995574,0.0003683828,0.0001346541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009965983,"about_ca_system_score_gemma":0.0005140777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01025394,"about_ca_topic_score_gemma":0.01034674,"domain_scores_codex":[0.999001,0.0005594774,0.00005599757,0.0001268185,0.0002116737,0.00004511885],"domain_scores_gemma":[0.9977271,0.001047662,0.0003359454,0.0004142846,0.0004252756,0.00004967317],"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":[0.0002653506,0.0001169865,0.1978201,0.0001472933,0.0008547005,0.0001078758,0.00008394106,0.7646363,0.005876843,0.002193124,0.0009038796,0.02699364],"study_design_scores_gemma":[0.00005354288,0.0001814474,0.18503,0.00003625533,0.0001841623,0.00003308615,0.00009780113,0.8003977,0.009810202,0.002266317,0.001840005,0.00006949002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734347,0.0003029018,0.01878483,0.0001150719,0.00002587416,0.00005159105,0.004261428,0.0003296093,0.002693941],"genre_scores_gemma":[0.9899273,0.00007701851,0.008433428,0.00001437183,0.000008759414,0.00003306702,0.001345921,0.00003566944,0.000124511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01025394,"threshold_uncertainty_score":0.02038848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2575114698127365,"score_gpt":0.363171461347964,"score_spread":0.1056599915352275,"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."}}