{"id":"W3175824399","doi":"10.1061/(asce)he.1943-5584.0002097","title":"Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E)","year":2021,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Global Institute for Water Security; University of Saskatchewan; University of Calgary; Environment and Climate Change Canada; University of Waterloo","funders":"Compute Canada; National Oceanic and Atmospheric Administration; Canada First Research Excellence Fund; Office of Research and Development; Global Water Futures; U.S. Environmental Protection Agency","keywords":"Environmental science; Watershed; Hydrology (agriculture); Surface runoff; Streamflow; Hydrological modelling; Calibration; Flooding (psychology); Coupled model intercomparison project; Shore; Climatology; Climate change; Computer science; Climate model; Geology; Ecology; Drainage basin","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":[0.004874312,0.0007637799,0.0008839142,0.0005959987,0.0008648472,0.001419787,0.001757174,0.001429773,0.003030902],"category_scores_gemma":[0.002401815,0.0004413652,0.001151621,0.001190188,0.0004719873,0.001120817,0.001891075,0.001098996,0.001091182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001927478,"about_ca_system_score_gemma":0.006337747,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0988095,"about_ca_topic_score_gemma":0.09817972,"domain_scores_codex":[0.9989949,0.0003317316,0.00003228787,0.0001478003,0.0003459627,0.0001474127],"domain_scores_gemma":[0.9985425,0.0002119765,0.0001828125,0.0003414751,0.0005025018,0.0002187733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.004351587,0.004715296,0.234327,0.0007372873,0.002166417,0.001023666,0.00111363,0.3448454,0.04137902,0.009422218,0.1830409,0.1728776],"study_design_scores_gemma":[0.007250582,0.003817305,0.4372749,0.0002280986,0.0006962683,0.0002297363,0.001201729,0.2897639,0.06516166,0.004137365,0.1898926,0.0003458906],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8079451,0.0005850646,0.03195206,0.003324583,0.0002518658,0.002876449,0.1031701,0.005130128,0.04476473],"genre_scores_gemma":[0.7207144,0.000332142,0.1018259,0.001489014,0.00008917696,0.002726908,0.145379,0.001552994,0.02589058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9011905,"threshold_uncertainty_score":0.1964688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0150559948834064,"score_gpt":0.2482539910784168,"score_spread":0.2331979961950104,"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."}}