{"id":"W1999378232","doi":"10.1061/(asce)1084-0699(2000)5:4(424)","title":"Performance Evaluation of Artificial Neural Networks for Runoff Prediction","year":2000,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Mean squared error; Statistic; Computer science; Surface runoff; Regression; Linear regression; Nonlinear system; Statistics; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001536616,0.00009421449,0.0001720993,0.00004120025,0.00004988646,0.000008317461,0.0001384553,0.00008334058,0.0005827297],"category_scores_gemma":[0.0001636786,0.00007453769,0.00008801341,0.0001436929,0.00004327606,0.000173972,0.00001690612,0.0001714624,0.000004429478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000927015,"about_ca_system_score_gemma":0.000005851259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002124646,"about_ca_topic_score_gemma":3.894468e-7,"domain_scores_codex":[0.9988683,0.00003598911,0.0004339367,0.0001029318,0.0003614227,0.0001973594],"domain_scores_gemma":[0.9995877,0.0000651824,0.0001791116,0.00008289249,0.00003311142,0.00005204869],"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":[0.00005937943,0.00002821322,0.002922862,0.000003944925,0.000008622825,9.495079e-7,0.00002878957,0.9415579,0.003962793,0.000001240259,0.00004751336,0.05137783],"study_design_scores_gemma":[0.0002630246,0.0005765059,0.01130719,0.00001580025,0.00005347175,0.00005074104,9.573913e-7,0.9869057,0.0004918322,0.00005338106,0.0002201443,0.00006125247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959298,0.00005407269,0.003267827,0.00005006333,0.000275363,0.0001306131,0.000001282986,0.00001989102,0.000271048],"genre_scores_gemma":[0.9980274,0.0000129059,0.001709998,0.00002428277,0.0001998092,0.000005099692,0.000001476744,0.000008222733,0.00001080098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05131657,"threshold_uncertainty_score":0.6380481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02266431642366356,"score_gpt":0.2318624977409896,"score_spread":0.209198181317326,"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."}}