{"id":"W2944685396","doi":"10.1007/s00477-019-01680-4","title":"Stepwise extreme learning machine for statistical downscaling of daily maximum and minimum temperature","year":2019,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Climate Extremes; National Research Foundation of Korea; Government of Canada","keywords":"Downscaling; Extreme learning machine; Artificial neural network; Computer science; Artificial intelligence; Machine learning; Feature selection; Feedforward neural network; Feature (linguistics); Climate change; Algorithm","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001377746,0.0002156021,0.000299641,0.00006819206,0.0003928617,0.00005931691,0.0001635885,0.0001226574,0.0008190749],"category_scores_gemma":[0.0001980055,0.0001693549,0.00004025632,0.0001029434,0.0008926475,0.0001137054,0.0005321568,0.0006971348,0.00004291832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001771124,"about_ca_system_score_gemma":0.00001623196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001569216,"about_ca_topic_score_gemma":0.00001828453,"domain_scores_codex":[0.997429,0.0002281244,0.0003136274,0.0006450686,0.0007756295,0.0006085699],"domain_scores_gemma":[0.9984277,0.0009451364,0.0001057773,0.0002179038,0.000007658807,0.0002958176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001038275,0.001179815,0.6643387,0.0002324261,0.0001585996,0.00002882279,0.0009325816,0.03687399,0.2261125,0.0007409561,0.0003215731,0.06804173],"study_design_scores_gemma":[0.006126323,0.01074941,0.4859889,0.0002231294,0.0001401258,0.00006445107,0.001337081,0.4693364,0.001058233,0.02231506,0.001594338,0.001066639],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987709,0.0001985882,0.01042852,0.00009862344,0.00005085461,0.0008273398,0.0001851406,0.00001736058,0.0004845387],"genre_scores_gemma":[0.9897685,0.0001793655,0.009313443,0.00001537578,0.00002480839,0.00004688209,0.00005757627,0.00002715476,0.0005668895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4324624,"threshold_uncertainty_score":0.8968295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0245642677246573,"score_gpt":0.3105415558625902,"score_spread":0.2859772881379329,"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."}}