{"id":"W4282919942","doi":"10.1029/2022rs007449","title":"On the Noise Estimation in Super Dual Auroral Radar Network Data","year":2022,"lang":"en","type":"article","venue":"Radio Science","topic":"Ionosphere and magnetosphere dynamics","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Japan Society for the Promotion of Science; Canadian Space Agency","keywords":"Radar; Noise (video); Computer science; Remote sensing; Data set; Ionosphere; Environmental science; Geology; Telecommunications; Geophysics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007058901,0.00007298101,0.00006916065,0.00001508786,0.0005251945,0.00006911492,0.0007463171,0.000006253242,0.001133866],"category_scores_gemma":[0.00001312979,0.00005695,0.00001698996,0.0006329439,0.0001330137,0.0002871716,0.0003555579,0.0001834098,0.00002569205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005230243,"about_ca_system_score_gemma":0.0001917636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002717984,"about_ca_topic_score_gemma":0.00001886905,"domain_scores_codex":[0.999005,0.00004807258,0.0001099505,0.000272178,0.00030003,0.0002647332],"domain_scores_gemma":[0.9993144,0.00007850807,0.00003742963,0.0005227492,0.00001149332,0.00003541037],"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":[0.00002864236,0.0001774261,0.009804889,0.000001950404,0.000007729805,0.00000693728,0.0006080121,0.2598032,0.0003162295,0.6559648,0.03243236,0.04084783],"study_design_scores_gemma":[0.0003308928,0.0000953152,0.008771607,0.000006421468,0.000007179387,0.000002804895,0.0005759088,0.9588521,0.00003645997,0.01992725,0.01118695,0.0002071761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973564,0.00003450082,0.007209709,0.001339361,0.0007190729,0.0002919702,0.00006258973,0.0000240996,0.01675471],"genre_scores_gemma":[0.9977861,3.508366e-7,0.001455804,0.000205876,0.00009656377,0.00001594192,0.00005202784,0.000005793248,0.00038153],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6990489,"threshold_uncertainty_score":0.9997792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01328564976088682,"score_gpt":0.240533569182646,"score_spread":0.2272479194217592,"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."}}