{"id":"W4281647787","doi":"10.5194/gmd-15-4425-2022","title":"Climate projections over the Great Lakes Region: using two-way coupling of a regional climate model with a 3-D lake model","year":2022,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Climate variability and models","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biological and Environmental Research; U.S. Geological Survey; National Oceanic and Atmospheric Administration; Nuclear Safety and Security Commission; Michigan Technological University; Office of Science; National Aeronautics and Space Administration; NOAA Great Lakes Environmental Research Laboratory; U.S. Department of Energy","keywords":"Coupled model intercomparison project; Climate model; Climatology; Climate change; Environmental science; Precipitation; General Circulation Model; GCM transcription factors; Downscaling; Representative Concentration Pathways; Atmospheric research; Meteorology; Geography; Geology; Oceanography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001712961,0.0003420277,0.0003208834,0.0001473032,0.002620107,0.0001009464,0.0006092241,0.00005995263,0.0002777204],"category_scores_gemma":[0.000009853986,0.0002627028,0.0001242241,0.0007649701,0.0005154738,0.0003588378,0.001277804,0.0003212219,0.00001187871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005249012,"about_ca_system_score_gemma":0.0003377018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001170139,"about_ca_topic_score_gemma":0.001329732,"domain_scores_codex":[0.9961174,0.00004959851,0.0006701163,0.001009853,0.001288271,0.0008648356],"domain_scores_gemma":[0.9986392,0.00004485957,0.0003289395,0.0007906659,0.00005788887,0.0001384288],"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":[0.0001403528,0.0002363702,0.001449863,0.00003814314,0.00001827262,0.000003768284,0.003554364,0.9913331,0.001418029,0.001132899,0.0004693118,0.0002055026],"study_design_scores_gemma":[0.0005492472,0.00002330134,0.0002213854,0.00004287694,0.000049623,0.0000504519,0.0003031568,0.9964385,0.00007860237,0.00117173,0.000701991,0.0003691244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8356642,0.00001463657,0.1618925,0.0001871791,0.0001435102,0.0007740255,0.0002324633,0.00008065151,0.001010905],"genre_scores_gemma":[0.95952,0.00002967924,0.03777231,0.0002788208,0.00001262472,0.0004163041,0.0001178739,0.00004955174,0.001802795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1241202,"threshold_uncertainty_score":0.9999825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05778196383852923,"score_gpt":0.2547140752094254,"score_spread":0.1969321113708962,"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."}}