{"id":"W4367333352","doi":"10.33012/navi.588","title":"A Baseband MLE for Snapshot GNSS Receiver Using Super-Long-Coherent Correlation in a Fractional Fourier Domain","year":2023,"lang":"en","type":"article","venue":"NAVIGATION Journal of the Institute of Navigation","topic":"GNSS positioning and interference","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Baseband; GNSS applications; Algorithm; Computer science; Snapshot (computer storage); Estimator; Global Positioning System; Frequency domain; Mathematics; Telecommunications; Statistics; Bandwidth (computing)","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":[0.0007217598,0.0005670094,0.000509755,0.0003188371,0.0002962651,0.0006072179,0.0004863504,0.0006874236,0.001182485],"category_scores_gemma":[0.002047365,0.0002798126,0.000452569,0.0004616419,0.0003772099,0.0009040073,0.0005353132,0.0008036248,0.0003947112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00037179,"about_ca_system_score_gemma":0.000787395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001495532,"about_ca_topic_score_gemma":0.002395702,"domain_scores_codex":[0.9996489,0.0000977556,0.00001504776,0.00006676723,0.0001411325,0.00003048384],"domain_scores_gemma":[0.9995246,0.0002321275,0.00004745208,0.0000533457,0.0001283749,0.00001415385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00034523,0.00008995742,0.003832209,0.0001823918,0.0001293537,0.0002770057,0.0002276922,0.5644628,0.08368564,0.03025042,0.002727323,0.31379],"study_design_scores_gemma":[0.0000110741,0.00003995306,0.0004635883,0.00001034859,0.00001231296,0.00009040989,0.00001173835,0.9865401,0.01023707,0.001437891,0.001129341,0.00001607797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01092807,0.0000921561,0.9881045,0.00007935878,0.00002098665,0.000008668455,0.0000242455,0.0002199606,0.0005220202],"genre_scores_gemma":[0.3376485,0.0002061527,0.6592532,0.0001360545,0.00003835917,0.00005615578,0.0001948086,0.0000889942,0.002377759],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001495532,"threshold_uncertainty_score":0.003955841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02710925511988723,"score_gpt":0.2751195550281458,"score_spread":0.2480102999082586,"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."}}