{"id":"W2793724872","doi":"10.1016/j.envsoft.2017.11.016","title":"A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a global observatory network","year":2018,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":172,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Environment; Ministry of the Environment, Conservation and Parks; University of Toronto","funders":"Natural Environment Research Council; Sight Research UK; Global Lake Ecological Observatory Network","keywords":"Environmental science; Thermocline; Observatory; Residence time (fluid dynamics); Climatology; Meteorology; Econometrics; Hydrology (agriculture); Geology; Mathematics; Geography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002557259,0.0003140037,0.0004241414,0.00001678055,0.0008204959,0.00002038355,0.0005227028,0.0001110859,0.0002652074],"category_scores_gemma":[0.00001137334,0.0002504125,0.0002251058,0.0005417971,0.001283183,0.0001599218,0.001061865,0.000178862,0.00007999378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001443041,"about_ca_system_score_gemma":0.000005577102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001342748,"about_ca_topic_score_gemma":0.004863412,"domain_scores_codex":[0.9978598,0.0001044166,0.0004014476,0.0006178562,0.0004037825,0.0006126453],"domain_scores_gemma":[0.9990954,0.00005798193,0.0002405059,0.0005121178,0.000005946088,0.00008808379],"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":[0.00001603107,0.00007131341,0.4608025,0.000002698723,0.0002245092,0.000001267107,0.001000583,0.5375686,0.00004229026,0.000005118551,0.00009648859,0.0001685409],"study_design_scores_gemma":[0.0002381563,0.00003504515,0.2908329,0.00001270774,0.0003155483,4.794149e-7,0.0001214981,0.7078012,0.00008540096,0.000200422,0.0001605012,0.0001962123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8462595,0.00005996152,0.1526267,0.00002715675,0.0001057073,0.0002455422,0.0003931733,0.0000551855,0.0002270845],"genre_scores_gemma":[0.9530205,0.00001477863,0.04605591,0.0003564914,0.00006007864,0.00002819454,0.00005852785,0.00001409924,0.0003914734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1702325,"threshold_uncertainty_score":0.9999948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05315431131901118,"score_gpt":0.2761532281447373,"score_spread":0.2229989168257261,"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."}}