{"id":"W2141685823","doi":"10.1109/jstsp.2009.2023349","title":"Collaborative Code Tracking of Composite GNSS Signals","year":2009,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"GNSS positioning and interference","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Computer science; Ranging; Channel (broadcasting); Jitter; Exploit; Satellite navigation; Real-time computing; GNSS augmentation; Code (set theory); Satellite system; Global Positioning System; 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":[0.0002059006,0.0001360675,0.0003311054,0.0002402019,0.00004714452,0.0000659857,0.0001852794,0.00008928392,0.000009352212],"category_scores_gemma":[0.00002684211,0.0001308241,0.00004296004,0.0007090899,0.00003413822,0.0003725691,0.000003381911,0.0004574178,6.171023e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008784246,"about_ca_system_score_gemma":0.0001162573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001273906,"about_ca_topic_score_gemma":0.000002535142,"domain_scores_codex":[0.9987747,0.00004842868,0.0006506426,0.00008563291,0.0002427666,0.0001978149],"domain_scores_gemma":[0.9987853,0.0000541178,0.0002647439,0.0000494022,0.0007910638,0.0000554305],"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":[0.00007717489,0.0001248446,0.0007869973,0.0001176919,0.00004023958,0.00003040579,0.002509387,0.1213613,0.837422,0.00004686341,0.0002005269,0.03728257],"study_design_scores_gemma":[0.0007729523,0.0005578352,0.00835692,0.002171712,0.00004122372,0.0001248263,0.0002121044,0.0460246,0.9396518,0.001729652,0.00009185397,0.0002644983],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9535547,0.001826657,0.04193573,0.00009409455,0.0001404589,0.00006565386,0.000003856623,0.00003887224,0.002339908],"genre_scores_gemma":[0.9966232,0.0000526636,0.003070845,0.00003685002,0.0001884355,5.677747e-7,9.283872e-7,0.00001193221,0.00001456921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1022298,"threshold_uncertainty_score":0.5334854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01372405737202454,"score_gpt":0.2636884673310222,"score_spread":0.2499644099589977,"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."}}