{"id":"W3029609325","doi":"10.3390/app10103613","title":"An Efficient Ultra-Tight GPS/RISS Integrated System for Challenging Navigation Environments","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"GNSS positioning and interference","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"Global Positioning System; GPS signals; Jamming; Precision Lightweight GPS Receiver; Computer science; Inertial measurement unit; Assisted GPS; GPS disciplined oscillator; Robustness (evolution); Time to first fix; Real-time computing; SIGNAL (programming language); GPS/INS; Telecommunications; Gps receiver; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001394747,0.000103927,0.00009867957,0.00002424634,0.0001972228,0.00007695991,0.0002145827,0.00003812891,0.000005133535],"category_scores_gemma":[0.00000345217,0.00008228643,0.00002155225,0.0001645023,0.00005561202,0.0000747678,0.000005890605,0.00007279532,0.0000334613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004529438,"about_ca_system_score_gemma":0.000006069416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003128389,"about_ca_topic_score_gemma":3.233138e-7,"domain_scores_codex":[0.9992698,0.000008196871,0.0001447338,0.0002325195,0.0001517015,0.0001929935],"domain_scores_gemma":[0.9997826,0.00002975099,0.00002679501,0.00006775777,0.000007297091,0.00008574836],"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":[0.000006580886,0.00001963994,0.00002715898,0.00007437664,0.000009636521,4.38216e-7,0.001882808,0.4261744,0.564138,0.004711269,0.00005030837,0.0029054],"study_design_scores_gemma":[0.0001175775,0.00008591511,0.00009837274,0.00006024725,0.0000068182,0.000001452567,0.001928736,0.7713137,0.2258948,0.00001528089,0.0003351132,0.0001419361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8276283,0.00006398735,0.165498,0.0000496595,0.0001816843,0.0002648248,0.00001538761,0.0003486494,0.005949532],"genre_scores_gemma":[0.9985584,0.000002496836,0.0012644,0.00002644066,0.00005156831,0.00005952659,0.00002765663,0.000006907705,0.00000259028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3451393,"threshold_uncertainty_score":0.3355543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01732247179894539,"score_gpt":0.2179699419034957,"score_spread":0.2006474701045503,"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."}}