{"id":"W4322623365","doi":"10.1029/2022sw003185","title":"A‐CHAIM: Near‐Real‐Time Data Assimilation of the High Latitude Ionosphere With a Particle Filter","year":2023,"lang":"en","type":"article","venue":"Space Weather","topic":"Ionosphere and magnetosphere dynamics","field":"Physics and Astronomy","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; New Brunswick Innovation Foundation","keywords":"Ionosphere; Total electron content; Data assimilation; Ionosonde; Latitude; Meteorology; Space weather; TEC; Middle latitudes; Atmospheric sciences; Environmental science; Electron density; Geology; Geodesy; Geophysics; Physics; Plasma","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001086008,0.0001348028,0.0001492225,0.000004666566,0.0001164282,0.00004438543,0.0003357095,0.00003697624,0.002655774],"category_scores_gemma":[0.000003834525,0.00008723427,0.00004659105,0.0003311919,0.00007856487,0.0001684583,0.0001967455,0.00009325918,0.0002889831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001209394,"about_ca_system_score_gemma":0.00007666981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001126582,"about_ca_topic_score_gemma":0.0002085662,"domain_scores_codex":[0.999167,0.00003947318,0.000140966,0.0002349714,0.0001846757,0.0002329681],"domain_scores_gemma":[0.9989611,0.00004412257,0.0001092549,0.0007988978,0.00004515728,0.00004148782],"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":[0.000180284,0.0005203609,0.7119868,0.00006315888,0.0006825469,0.000005601593,0.002847272,0.06487308,0.01130685,0.08936401,0.09709691,0.02107317],"study_design_scores_gemma":[0.00321754,0.0003250312,0.462515,0.0001917097,0.0003259644,0.00000171766,0.002001092,0.4729255,0.004242362,0.007753205,0.04550221,0.0009986639],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977635,0.000008653485,0.001668207,0.001328022,0.000113164,0.0003008775,0.0001391659,0.00008312397,0.01872382],"genre_scores_gemma":[0.9779203,0.000002311748,0.002032991,0.00003127871,0.0001165813,0.00001355237,0.000114939,0.00003450015,0.01973353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4080524,"threshold_uncertainty_score":0.9982559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01022840386625823,"score_gpt":0.2222645216060337,"score_spread":0.2120361177397755,"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."}}