{"id":"W4392585647","doi":"10.5194/egusphere-egu24-4140","title":"Integrating Deterministic and Probabilistic Approaches for Improved Hydrological Predictions: Insights from Multi-model Assessment in the Great Lakes Watersheds","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Probabilistic logic; Computer science; Environmental science; Hydrology (agriculture); Artificial intelligence; Geology; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004220054,0.0005635475,0.0004228189,0.0009482517,0.0004377709,0.001515235,0.0008560634,0.0008995432,0.0005199842],"category_scores_gemma":[0.0102017,0.0003336167,0.0007065557,0.0007921975,0.0006789898,0.001757667,0.001082784,0.0006980899,0.00004647134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001860878,"about_ca_system_score_gemma":0.00168327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0361756,"about_ca_topic_score_gemma":0.04498996,"domain_scores_codex":[0.9992193,0.0004671498,0.00004410584,0.00008424482,0.0001382856,0.00004694796],"domain_scores_gemma":[0.9961635,0.002875789,0.0002899068,0.0001928064,0.00040234,0.00007567707],"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":[0.00004193777,0.00007440126,0.02845443,0.00004140098,0.000106666,0.0001322871,0.0002454002,0.9552621,0.0007229103,0.003243835,0.0002512674,0.01142338],"study_design_scores_gemma":[0.000003615851,0.00002423666,0.006420952,0.000006966093,0.00001599449,0.000009505045,0.00008663801,0.9911761,0.0002252455,0.001885474,0.0001341695,0.00001110809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829518,0.0001612492,0.01418935,0.0008175527,0.000005313568,0.00003248601,0.0001428872,0.00008762009,0.001611731],"genre_scores_gemma":[0.9941137,0.0000586711,0.005578381,0.00002625271,0.000005225724,0.00001211359,0.00007126392,0.00001349025,0.0001209104],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0361756,"threshold_uncertainty_score":0.07193011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05438199352495589,"score_gpt":0.2739141466244111,"score_spread":0.2195321530994552,"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."}}