{"id":"W3037280355","doi":"10.1007/1345_2020_118","title":"Enhancing Navigation in Difficult Environments with Low-Cost, Dual-Frequency GNSS PPP and MEMS IMU","year":2020,"lang":"en","type":"book-chapter","venue":"International Association of Geodesy symposia","topic":"GNSS positioning and interference","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Helmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum GFZ; Natural Sciences and Engineering Research Council of Canada; Centre National d’Etudes Spatiales; York University","keywords":"GNSS applications; Precise Point Positioning; Inertial measurement unit; Air navigation; Computer science; Real-time computing; Inertial navigation system; Satellite navigation; Real Time Kinematic; Global Positioning System; Remote sensing; Engineering; Telecommunications; Artificial intelligence; Inertial frame of reference; Geography; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001262817,0.0002872747,0.0003246417,0.0001567896,0.00003433737,0.00006009547,0.00015263,0.0002665245,0.00006756628],"category_scores_gemma":[0.00003820958,0.0003073221,0.00006207234,0.00004381437,0.00003347506,0.0002294039,0.00003974323,0.0003914908,0.00004966233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009756021,"about_ca_system_score_gemma":0.00002736365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000056319,"about_ca_topic_score_gemma":0.00009759183,"domain_scores_codex":[0.9984071,0.00001403899,0.0005478486,0.0003024514,0.0005608446,0.000167679],"domain_scores_gemma":[0.9992422,0.00009312023,0.0003605208,0.0001122236,0.0001255636,0.00006630291],"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":[0.000430641,0.000599907,0.07667048,0.001713949,0.006445335,0.0002965074,0.0138412,0.07622918,0.4287693,0.3838359,0.003647441,0.007520165],"study_design_scores_gemma":[0.02670718,0.002733442,0.2248521,0.05620447,0.002182778,0.0005413538,0.001017246,0.1396586,0.333267,0.06696369,0.1300455,0.01582656],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3505531,0.0007794263,0.004781291,0.002512011,0.002210498,0.001620281,0.00111288,0.0004656324,0.6359649],"genre_scores_gemma":[0.9853743,0.0002899897,0.000230302,0.00004807369,0.0001334277,0.00003328938,0.0005983015,0.0000578522,0.01323452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6348212,"threshold_uncertainty_score":0.9999379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004040053266029426,"score_gpt":0.1843953474494083,"score_spread":0.1803552941833789,"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."}}