{"id":"W4362701584","doi":"10.3390/ecws-7-14237","title":"Short-Term Precipitation Forecasting Based on the Improved Extreme Learning Machine Technique","year":2023,"lang":"en","type":"article","venue":"","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Ottawa; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extreme learning machine; Regularization (linguistics); Term (time); Precipitation; Computer science; Quantitative precipitation forecast; Orthogonality; Mean squared error; Artificial intelligence; Mathematics; Algorithm; Machine learning; Meteorology; Statistics; Artificial neural network; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.00120588,0.0001361931,0.00009717003,0.0001541162,0.0003665532,0.0001723921,0.000534009,0.00005039726,0.00004236816],"category_scores_gemma":[0.0003646361,0.00008841506,0.00006297338,0.0006210392,0.00001736017,0.0001389984,0.0001549833,0.0003150394,0.00006825372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002382906,"about_ca_system_score_gemma":0.00002293225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003797076,"about_ca_topic_score_gemma":0.00001167418,"domain_scores_codex":[0.9987936,0.0001867686,0.0001733275,0.0003292784,0.0002306339,0.000286412],"domain_scores_gemma":[0.9989007,0.0005551534,0.00005828492,0.0003947556,0.00004229002,0.00004883352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002471004,0.00007814137,0.01490934,0.00004685519,0.00001893224,0.00002523923,0.001440831,0.05328695,0.01222444,0.009276205,0.001589564,0.9070788],"study_design_scores_gemma":[0.0000879025,0.0001215869,0.006453292,0.00002853485,0.000002057734,0.0000033875,0.00001951892,0.9908567,0.001123136,0.0003071722,0.0008747675,0.0001219361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03336208,0.00000485458,0.9487966,0.003810896,0.0001508181,0.000371396,5.077962e-7,0.001620463,0.01188244],"genre_scores_gemma":[0.9821774,0.000001174025,0.01489677,0.0002590372,0.00005057092,0.0001028541,0.000008877927,0.00001608316,0.002487289],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9488153,"threshold_uncertainty_score":0.3605461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0480323980886057,"score_gpt":0.2637467066488954,"score_spread":0.2157143085602897,"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."}}