{"id":"W2885136423","doi":"10.1080/14634988.2018.1500850","title":"Detection of spatial and temporal hydro-meteorological trends in Lake Michigan, Lake Huron and Georgian Bay","year":2018,"lang":"en","type":"article","venue":"Aquatic Ecosystem Health & Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Environment and Climate Change Canada; Mitacs; Government of Canada","keywords":"Environmental science; Precipitation; Bay; Surface runoff; Wind speed; Cloud cover; Snow; Climatology; Climate change; Drainage basin; Hydrology (agriculture); Oceanography; Meteorology; Geology; Geography; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001126781,0.0001832164,0.0003503165,0.0001850116,0.0001986032,0.00001312846,0.0001177895,0.00006681955,0.0002559951],"category_scores_gemma":[0.00000933456,0.0001574049,0.00002719921,0.0002209556,0.0001964964,0.0001010728,0.0003447161,0.00009160841,0.00006606247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004054178,"about_ca_system_score_gemma":0.000002174632,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009659981,"about_ca_topic_score_gemma":0.4525521,"domain_scores_codex":[0.9981912,0.0002337347,0.0005134532,0.0004621994,0.0002008152,0.0003985304],"domain_scores_gemma":[0.9994417,0.00003755976,0.0002047987,0.0002178291,0.000003079841,0.00009499288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001622674,0.0002136819,0.8653563,0.0005152058,0.00009856555,0.00003019949,0.002057877,0.00008945396,0.00005769817,0.0002743649,0.0003214679,0.1308229],"study_design_scores_gemma":[0.001266185,0.001057003,0.9638104,0.0000697433,0.00004580911,0.00000569028,0.0003178271,0.01192314,0.00004389722,0.0006690528,0.02056913,0.000222095],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991913,0.00007017945,0.001123591,0.001934727,0.0001689581,0.0005597267,0.000008110624,0.00003447503,0.004187186],"genre_scores_gemma":[0.9986225,0.00009673367,0.0003537164,0.0004748325,0.0000329893,0.00004837861,0.00001075841,0.00001004616,0.0003500908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4515862,"threshold_uncertainty_score":0.6418784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01114864178167178,"score_gpt":0.2392542620467851,"score_spread":0.2281056202651133,"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."}}