{"id":"W2089425135","doi":"10.1016/s0380-1330(01)70650-8","title":"Long-term Trends in the Seasonal Cycle of Great Lakes Water Levels","year":2001,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":94,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Oceanic and Atmospheric Administration; University of Wisconsin-Madison","keywords":"Snowmelt; Environmental science; Surface runoff; Spring (device); Precipitation; Climate change; Trend analysis; Seasonality; Water cycle; Climatology; Period (music); Water year; Global warming; Annual cycle; Hydrology (agriculture); Physical geography; Snow; Geography; Oceanography; Drainage basin; Ecology; Geology; Meteorology","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":[0.0004532533,0.0001194508,0.0001506697,0.0005426014,0.000308086,0.0005616452,0.0002416732,0.0005158937,0.001493046],"category_scores_gemma":[0.001747751,0.0001996358,0.0002239171,0.0008185028,0.0003066957,0.0006030058,0.0003963029,0.0004860751,0.00040478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005710337,"about_ca_system_score_gemma":0.0003869219,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02326312,"about_ca_topic_score_gemma":0.06399282,"domain_scores_codex":[0.9998767,0.00001790124,0.0000192883,0.00003038797,0.00002696958,0.00002869632],"domain_scores_gemma":[0.9986755,0.000271481,0.0004108809,0.00009948198,0.0003503255,0.0001922806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002293069,0.00006751652,0.989435,0.00002570037,0.0001510611,0.00007092128,0.0004554987,0.0004600538,0.002365021,0.0001624606,0.0008892705,0.005688238],"study_design_scores_gemma":[0.000002659053,0.00002475266,0.9990898,0.000002623617,0.0000142103,0.00001722624,0.00009593117,0.0003185421,0.00006772943,0.00002479518,0.0003390679,0.000002676797],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983071,0.000122127,0.00006546632,0.0002455,0.000008301799,0.0000024391,0.0007430895,0.000007786935,0.0004981357],"genre_scores_gemma":[0.9982198,0.0001017049,0.00005398375,0.00003995074,0.00001204777,0.000004485385,0.0009413838,0.000003536489,0.0006230313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9767369,"threshold_uncertainty_score":0.04625547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06373402542423852,"score_gpt":0.339570514375526,"score_spread":0.2758364889512875,"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."}}