{"id":"W2902899242","doi":"10.1007/s10291-018-0806-y","title":"Improvement of carrier phase tracking in high dynamics conditions using an adaptive joint vector tracking architecture","year":2018,"lang":"en","type":"article","venue":"GPS Solutions","topic":"GNSS positioning and interference","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Control theory (sociology); Robustness (evolution); GNSS applications; Computer science; Kalman filter; Extended Kalman filter; Covariance matrix; Tracking (education); Global Positioning System; Algorithm; Artificial intelligence; 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":[0.00009458514,0.0001383381,0.0001706357,0.0001863399,0.0001816549,0.00002924715,0.0000933237,0.00007619119,0.00005950798],"category_scores_gemma":[0.00002534156,0.0001543314,0.00005514812,0.0002172352,0.0001354278,0.0001917398,0.00002128178,0.0002337088,0.000003949567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002823008,"about_ca_system_score_gemma":0.00004282243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004335109,"about_ca_topic_score_gemma":0.001061688,"domain_scores_codex":[0.9990988,0.00002655357,0.0002933816,0.0001704597,0.0001051153,0.0003056822],"domain_scores_gemma":[0.9995403,0.0000237714,0.00005320738,0.0001967906,0.000113536,0.00007235393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000311209,0.0005620744,0.0001164358,0.00006171224,0.0001149967,0.000006198675,0.0056979,0.3159173,0.6584181,0.01203508,0.0001027157,0.006936378],"study_design_scores_gemma":[0.0009689956,0.0007566028,0.003972228,0.0003456852,0.00006739372,0.00002253004,0.001218568,0.9341211,0.05598006,0.002164282,0.00002221571,0.0003602859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.810699,0.00003844042,0.1876952,0.00002264896,0.0002876662,0.000135025,0.0005628701,0.00009979158,0.0004594062],"genre_scores_gemma":[0.9980848,0.000002207598,0.001651915,0.00001298605,0.0001018849,0.00001903646,0.0001002866,0.00002006495,0.000006789616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6182038,"threshold_uncertainty_score":0.6293453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0422802098041424,"score_gpt":0.2843724656011744,"score_spread":0.2420922557970319,"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."}}