{"id":"W4392587707","doi":"10.5194/egusphere-egu24-2896","title":"Seasonal Precipitation Forecast Using an Ensemble of Artificial Neural Networks and Climate Oscillation Indices. A Case Study of Ceará, northeastern Brazil.","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Precipitation; Climatology; Environmental science; Artificial neural network; Meteorology; Oscillation (cell signaling); El Niño Southern Oscillation; Geography; Computer science; Artificial intelligence; Geology; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008033107,0.0003109712,0.0004323809,0.0001132594,0.0001546883,0.00010549,0.0001644689,0.0002693023,0.0001506499],"category_scores_gemma":[0.00004812447,0.0002712373,0.00008144758,0.0002806889,0.0002370395,0.0001580302,0.001354713,0.0004822802,0.000006039754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001048216,"about_ca_system_score_gemma":0.000021908,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005892077,"about_ca_topic_score_gemma":0.01872878,"domain_scores_codex":[0.997382,0.0002673193,0.0007126926,0.0007612277,0.0005014088,0.0003753825],"domain_scores_gemma":[0.9988853,0.000102118,0.0004808645,0.0003447352,0.00003665716,0.0001503099],"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":[0.00007969826,0.0003123154,0.1126678,0.00009765495,0.00002882473,0.00006818547,0.00334347,0.8617104,0.0006160146,0.000004490435,0.000002389212,0.02106873],"study_design_scores_gemma":[0.0001844747,0.0004307471,0.009858416,0.00007869794,0.0001507289,0.0002605365,0.0004310973,0.9878972,0.00002698307,0.0004364313,0.000001701408,0.0002430285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968228,0.00002946338,0.001734157,0.00001716156,0.0003340871,0.0007972926,0.00003758394,0.00006513079,0.0001622809],"genre_scores_gemma":[0.9977083,0.000002410152,0.002071848,0.00001640817,0.00009983598,0.00001106358,0.00004135658,0.00003566131,0.00001310892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1261867,"threshold_uncertainty_score":0.999974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06575865200371933,"score_gpt":0.3162712856760991,"score_spread":0.2505126336723797,"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."}}