{"id":"W2049197770","doi":"10.1109/plans.2010.5507258","title":"Improving carrier phase reacquisition using advanced receiver architectures","year":2010,"lang":"en","type":"article","venue":"IEEE/ION Position, Location and Navigation Symposium","topic":"GNSS positioning and interference","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; General Motors of Canada","keywords":"GNSS applications; Computer science; Lock (firearm); Process (computing); Carrier recovery; Phase (matter); Real-time computing; Tracking (education); Demodulation; Engineering; Telecommunications; Global Positioning System","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001733115,0.0002291598,0.000160334,0.0001862521,0.0003225731,0.0001425687,0.00009183525,0.0001738958,0.00003994952],"category_scores_gemma":[0.0000152107,0.0002502401,0.00004647612,0.0002896644,0.00009035019,0.0004376612,0.00001121271,0.0003315901,0.00002053968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008556603,"about_ca_system_score_gemma":0.00003626905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004910745,"about_ca_topic_score_gemma":0.000008446055,"domain_scores_codex":[0.9988136,0.00005103244,0.000370613,0.0003188669,0.0002127901,0.0002331026],"domain_scores_gemma":[0.9991356,0.00003625669,0.0001108733,0.0002442384,0.0003236987,0.000149309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003643474,0.00004916426,0.00006336359,0.00007690804,0.00001432105,0.00000114343,0.00065726,0.01034598,0.9818497,0.0003564386,0.0001697934,0.006379514],"study_design_scores_gemma":[0.001527109,0.0001575996,0.001140261,0.0002791239,0.00007001884,0.0002141995,0.000122407,0.2354485,0.7596753,0.0004119584,0.0004204345,0.000533005],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9443862,0.00007980071,0.05264289,0.0001606096,0.001319615,0.0002803649,0.00006120703,0.0003865544,0.0006827979],"genre_scores_gemma":[0.9978771,0.00002512578,0.001167544,0.0001299282,0.0002961388,0.00004299369,0.0003787597,0.00003274645,0.00004972688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2251026,"threshold_uncertainty_score":0.999995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006268962595086085,"score_gpt":0.2478853862774038,"score_spread":0.2416164236823177,"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."}}