{"id":"W2146077510","doi":"10.1007/s11200-007-0015-6","title":"A neural network approach for regional vertical total electron content modelling","year":2007,"lang":"en","type":"article","venue":"Studia Geophysica et Geodaetica","topic":"Ionosphere and magnetosphere dynamics","field":"Physics and Astronomy","cited_by":87,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"TEC; Total electron content; GNSS applications; Ionosphere; Artificial neural network; Global Positioning System; Satellite; International Reference Ionosphere; Geodesy; Computer science; Remote sensing; Geology; Telecommunications; Geophysics; Physics; Artificial intelligence","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":[0.0002568347,0.0003241187,0.0003884167,0.00001696649,0.000262559,0.00005372912,0.0001929617,0.0000679064,0.0000415901],"category_scores_gemma":[0.000004777315,0.0003058289,0.0003199483,0.0001977647,0.0001083232,0.0001232039,0.00009582934,0.0003199928,0.00002269585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000360267,"about_ca_system_score_gemma":0.0001075204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001849296,"about_ca_topic_score_gemma":0.000007150218,"domain_scores_codex":[0.9978244,0.00003729911,0.0003618025,0.0005016473,0.0002610019,0.00101388],"domain_scores_gemma":[0.9990631,0.0002390026,0.00007210797,0.0002934009,0.0001586137,0.0001737296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005930548,0.001015,0.001010123,0.00002907226,0.0003862064,0.000002759151,0.0003782862,0.1253104,0.0004534149,0.8600181,0.004672907,0.006130617],"study_design_scores_gemma":[0.004706909,0.00156649,0.009024197,0.00003888787,0.0004793594,0.000007714059,0.001418927,0.9252313,0.0004244494,0.04522105,0.01011187,0.001768874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4322769,0.0001179302,0.5352877,0.0004337564,0.0002544655,0.0009016379,0.00002063685,0.00008843856,0.03061855],"genre_scores_gemma":[0.9848652,0.000003170109,0.01214578,0.0002730025,0.001563287,0.0001301182,0.0001835479,0.00005121201,0.0007846711],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8147971,"threshold_uncertainty_score":0.9999394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0217585217691553,"score_gpt":0.2458017274187035,"score_spread":0.2240432056495482,"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."}}