{"id":"W4410193724","doi":"10.1016/j.asr.2025.05.011","title":"A deep learning model for correcting nonlinear biases between the TEC measurements of COSMIC-2 and GNSS","year":2025,"lang":"en","type":"article","venue":"Advances in Space Research","topic":"Earthquake Detection and Analysis","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute on Governance","funders":"Applied Basic Research Key Project of Yunnan; National Natural Science Foundation of China; National Aeronautics and Space Administration","keywords":"GNSS applications; TEC; COSMIC cancer database; Nonlinear system; Remote sensing; Computer science; Global Positioning System; Environmental science; Physics; Astronomy; Geology; Ionosphere; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.002060432,0.00007143951,0.0001662704,0.0002662602,0.0003267248,0.00004010966,0.0001624834,0.00003745128,0.00002275874],"category_scores_gemma":[0.001519722,0.00005155304,0.00004042575,0.0008311588,0.0001691943,0.0001582152,0.00002396181,0.0003040369,0.000003098638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007069952,"about_ca_system_score_gemma":0.00004868708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006431559,"about_ca_topic_score_gemma":0.01425299,"domain_scores_codex":[0.9987709,0.0002139422,0.0001809589,0.0002102427,0.000338509,0.000285448],"domain_scores_gemma":[0.9976845,0.001953342,0.00005231823,0.0001153247,0.0001533708,0.00004116786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003165984,0.000005272327,0.6184837,0.00003223754,0.00001207187,2.412442e-7,0.000156043,0.1038506,0.0000231464,0.000005036302,0.000006472009,0.2773934],"study_design_scores_gemma":[0.0003522456,0.000103222,0.03117312,0.000125206,0.00001398143,4.774474e-7,0.002695839,0.961971,0.001215285,0.0008630549,0.001407313,0.00007919806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9477434,0.01349709,0.030756,0.0009304874,0.0001102212,0.0006148646,0.00002379554,0.00003147419,0.006292731],"genre_scores_gemma":[0.9964426,0.0007489544,0.002184723,0.00001752225,0.0000279878,0.000005430003,0.000009337832,0.000002431733,0.0005610148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8581204,"threshold_uncertainty_score":0.7953502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1144929045101606,"score_gpt":0.3911737283272412,"score_spread":0.2766808238170806,"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."}}