{"id":"W4389943958","doi":"10.1029/2023sw003501","title":"Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions","year":2023,"lang":"en","type":"article","venue":"Space Weather","topic":"Ionosphere and magnetosphere dynamics","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Georgia Institute of Technology; Advanced Research Projects Agency; Defense Advanced Research Projects Agency; U.S. Department of the Interior","keywords":"International Reference Ionosphere; Earth's magnetic field; Artificial neural network; Geomagnetic latitude; Ionosphere; Empirical modelling; Satellite; Electron density; Meteorology; Computer science; Electron; Physics; Machine learning; Simulation; Total electron content; Geophysics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000346366,0.0003605077,0.0002579476,0.0003154282,0.0001654733,0.0004712714,0.0005490737,0.0004161145,0.000917046],"category_scores_gemma":[0.001588773,0.0001936556,0.0002686782,0.0002996495,0.0002541179,0.0005667606,0.000234363,0.0003767063,0.0001216759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007707675,"about_ca_system_score_gemma":0.0005081304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05225467,"about_ca_topic_score_gemma":0.02894637,"domain_scores_codex":[0.9999158,0.00002319809,0.000003963718,0.00002278966,0.00001939053,0.00001481323],"domain_scores_gemma":[0.9996117,0.0001957708,0.00004795639,0.00002706677,0.0001047602,0.00001282779],"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":[0.00001150951,0.000007124462,0.002262799,0.000003679834,0.00000679432,0.000007739513,0.000003538619,0.9955146,0.0001431284,0.0001969305,0.00006190775,0.001780229],"study_design_scores_gemma":[7.779878e-7,0.000001650393,0.0003191411,7.290072e-7,7.392364e-7,9.310709e-7,0.000001080931,0.9995348,0.00004464295,0.00007701459,0.00001764748,8.047886e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9075756,0.0002550865,0.08470747,0.0003240478,0.00003858943,0.00002321817,0.0004225827,0.0002924377,0.006361044],"genre_scores_gemma":[0.9960122,0.00004454611,0.00304824,0.00001407478,0.00000663135,0.00001057521,0.0001064489,0.000006969613,0.0007504092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05225467,"threshold_uncertainty_score":0.103901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02103179979045949,"score_gpt":0.2694644350263738,"score_spread":0.2484326352359143,"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."}}