{"id":"W2087937041","doi":"10.1061/(asce)he.1943-5584.0000542","title":"Discussion of “Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms” by Jan Adamowski and Christina Karapataki","year":2012,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial neural network; Multivariate statistics; Computer science; Artificial intelligence; Machine learning; Regression; Algorithm; Econometrics; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002002255,0.0001886567,0.0005001116,0.00007352185,0.00008416332,0.00001000841,0.0001115582,0.0001274002,0.00001795481],"category_scores_gemma":[0.0005558412,0.0001004946,0.00008195194,0.00007949684,0.0001170293,0.000186405,0.0001326293,0.000287549,6.982216e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004542977,"about_ca_system_score_gemma":0.000002604907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001045642,"about_ca_topic_score_gemma":7.483202e-7,"domain_scores_codex":[0.9982004,0.0001521576,0.0007578404,0.0001661049,0.0003996914,0.0003238556],"domain_scores_gemma":[0.998839,0.0001857471,0.0007178817,0.00008728,0.00004450042,0.0001255597],"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":[0.000140876,0.0001531554,0.05112464,0.00005670953,0.00002245525,6.76768e-7,0.0009427104,0.7707545,0.159547,0.000002678257,0.00002467882,0.01722994],"study_design_scores_gemma":[0.0005738079,0.0009611585,0.01269302,0.0001348303,0.000116578,0.00002578083,0.00003749601,0.9653196,0.01992646,0.00005965257,0.00003617434,0.0001154415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859081,0.0006562759,0.01300499,0.00005584844,0.0001637754,0.0001902224,0.000002652252,0.000009147312,0.000009023692],"genre_scores_gemma":[0.9980609,0.00001037866,0.001802531,0.000002322312,0.00009349229,0.000003884326,0.00000787244,0.00001409658,0.000004514476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1945651,"threshold_uncertainty_score":0.4098051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04163679777494468,"score_gpt":0.2839894611181368,"score_spread":0.2423526633431921,"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."}}