{"id":"W2134854911","doi":"10.1109/taes.2010.5417156","title":"Composite GNSS Signal Acquisition over Multiple Code Periods","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"GNSS positioning and interference","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Computer science; False alarm; Satellite navigation; Global Positioning System; Monte Carlo method; Statistical power; Code (set theory); Data acquisition; Algorithm; Real-time computing; Sensor fusion; Constant false alarm rate; Electronic engineering; Artificial intelligence; Engineering; Telecommunications; Mathematics; Statistics","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":[0.00009510395,0.0001978133,0.0001860487,0.00008165993,0.0002227231,0.000135761,0.00008754605,0.0001459952,0.00006863634],"category_scores_gemma":[5.943968e-7,0.0002000536,0.00006396444,0.0001122901,0.00004854662,0.0001297857,5.954525e-7,0.0005693857,0.00005433814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008663847,"about_ca_system_score_gemma":0.00002178106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001426859,"about_ca_topic_score_gemma":0.0003059861,"domain_scores_codex":[0.9990453,0.00002641069,0.000175783,0.0002184324,0.000141668,0.0003923991],"domain_scores_gemma":[0.9995978,0.00005081928,0.00002645181,0.0001910313,0.00003432452,0.00009960221],"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":[0.00005964412,0.00009409671,0.0002485867,0.00007720335,0.0001229086,0.000001942128,0.0004973657,0.1082582,0.8884145,0.000368547,0.0005657649,0.001291246],"study_design_scores_gemma":[0.001716186,0.0006003418,0.001604126,0.0002537984,0.0001014355,0.0002457552,0.000239459,0.7431939,0.2464173,0.00003623004,0.004776543,0.0008148889],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7695982,0.0002599727,0.227973,0.00003354485,0.0009242079,0.0001694638,0.00003285639,0.0002743166,0.0007344062],"genre_scores_gemma":[0.9991772,0.0001012686,0.0000477331,0.00003163536,0.00009695851,0.00006848795,0.000005728652,0.0000356447,0.0004353322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6419972,"threshold_uncertainty_score":0.8157948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00528248884681594,"score_gpt":0.2034497954788189,"score_spread":0.1981673066320029,"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."}}