{"id":"W3048798641","doi":"10.1016/j.jglr.2020.07.015","title":"Corrigendum to “Development and application of a North American Great Lakes hydrometeorological database — Part I: Precipitation, evaporation, runoff, and air temperature” [J. Great Lakes Res. 41 (2015) 65–77]","year":2020,"lang":"en","type":"erratum","venue":"Journal of Great Lakes Research","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Hydrometeorology; Surface runoff; Precipitation; Environmental science; Evaporation; Hydrology (agriculture); Climatology; Meteorology; Geology; Geography; Geotechnical engineering; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003256036,0.001673074,0.001430973,0.003871669,0.002662256,0.00321917,0.00281269,0.003409164,0.2267555],"category_scores_gemma":[0.03400919,0.0009546971,0.001285736,0.002791812,0.001088291,0.002298548,0.003104007,0.004355785,0.140072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003512925,"about_ca_system_score_gemma":0.006405257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09520829,"about_ca_topic_score_gemma":0.1294461,"domain_scores_codex":[0.996662,0.0004022669,0.0005634273,0.0004059605,0.001726439,0.0002399442],"domain_scores_gemma":[0.9663833,0.003548225,0.001024693,0.00117927,0.02665263,0.001211853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005803622,0.000003984843,0.00004003621,0.00002176017,0.000001364714,0.00001981184,0.000003706261,0.00001521716,0.00001398811,0.00008630882,0.9980798,0.001708295],"study_design_scores_gemma":[0.00002159721,0.00001399295,0.001420586,0.0001353484,0.00001105839,0.0000540083,0.00005017223,0.0001871217,0.0001524292,0.000380619,0.9975486,0.00002445831],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0003305244,0.0009066863,0.00154314,0.08816734,0.8623807,0.0001510946,0.02133385,0.001912282,0.0232743],"genre_scores_gemma":[0.00490686,0.003305855,0.005014366,0.08158077,0.1016956,0.0004618492,0.04664963,0.002652365,0.7537326],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.2267555,"threshold_uncertainty_score":0.7585728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03901169413156323,"score_gpt":0.3017209030239987,"score_spread":0.2627092088924355,"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."}}