{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006762832,0.0005295408,0.0003744109,0.0006145138,0.0001851606,0.0004777443,0.0003005253,0.0002767802,0.0005706816],"category_scores_gemma":[0.002197641,0.0002107558,0.0002789454,0.0004786808,0.000466859,0.0006073351,0.0006782031,0.0004668587,0.0001784781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003891215,"about_ca_system_score_gemma":0.0005152581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007972392,"about_ca_topic_score_gemma":0.001469502,"domain_scores_codex":[0.9992719,0.00009826027,0.00001977603,0.0001150824,0.0004396288,0.00005523104],"domain_scores_gemma":[0.9988427,0.0003975511,0.0002024757,0.0002024114,0.0002956796,0.00005918318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003243618,0.000285008,0.08003911,0.0003696407,0.0002462334,0.0009236807,0.0003294184,0.2884984,0.2073315,0.01563588,0.0007294471,0.4023681],"study_design_scores_gemma":[0.00008142083,0.001598029,0.09630276,0.00003572668,0.000140382,0.001609016,0.0001018805,0.7779426,0.1114559,0.007178216,0.00346094,0.0000931187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6306937,0.000291349,0.3651374,0.0000748072,0.00003585005,0.00009285611,0.0001335443,0.0003244819,0.003215983],"genre_scores_gemma":[0.9612827,0.0000951113,0.0374858,0.00002293958,0.0000175536,0.00002488454,0.0001361401,0.00002195562,0.0009129174],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0007972392,"threshold_uncertainty_score":0.003576577,"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."}}