{"id":"W2015686972","doi":"10.1139/s04-047","title":"El Niño southern-oscillation prediction using southern oscillation index and Niño3 as onset indicators: Application of artificial neural networks","year":2005,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Climate variability and models","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Oceanic and Atmospheric Administration","keywords":"El Niño Southern Oscillation; Oscillation (cell signaling); Teleconnection; Artificial neural network; Climatology; Southern oscillation; Multilayer perceptron; Index (typography); Environmental science; Multivariate ENSO index; Correlation coefficient; Pacific decadal oscillation; Lead time; Meteorology; Computer science; Statistics; Mathematics; Geology; Geography; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0006697961,0.0001057628,0.0001273311,0.0001251908,0.0001427832,0.00003287431,0.0001021747,0.00006501914,0.00004039884],"category_scores_gemma":[0.00002930558,0.00009753889,0.00003077545,0.0002082701,0.0003348805,0.0004519325,0.00008244948,0.0001360452,0.000002785093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001745123,"about_ca_system_score_gemma":0.000009348823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005187167,"about_ca_topic_score_gemma":0.000004411152,"domain_scores_codex":[0.9988586,0.00001782075,0.0003597292,0.0001963739,0.0004025493,0.0001649397],"domain_scores_gemma":[0.9994946,0.0000310228,0.0002469109,0.0001019576,0.000004540566,0.0001209685],"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":[0.00001920166,0.00003354096,0.1148584,0.000003583462,0.000002917074,2.540223e-7,0.0007390509,0.7845224,0.09235801,0.000008382939,4.840314e-7,0.007453837],"study_design_scores_gemma":[0.0001506834,0.00006362847,0.101638,0.00001159932,0.00001491755,0.0000500527,0.0002317632,0.8973036,0.0003581787,0.00005876615,0.00003804488,0.00008076706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9712842,0.0000518146,0.02842513,0.00003543856,0.00006289127,0.00009836107,0.00001526479,0.000007854444,0.00001902728],"genre_scores_gemma":[0.9988546,0.00002641408,0.001008508,0.00001519238,0.00008046919,9.798275e-7,0.000001528993,0.000007708776,0.000004539575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1127812,"threshold_uncertainty_score":0.397752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007448943745264915,"score_gpt":0.2071491046936846,"score_spread":0.1997001609484196,"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."}}