{"id":"W4389739612","doi":"10.5194/isprs-archives-xlviii-1-w2-2023-821-2023","title":"THE DEVELOPMENT AND VALIDATION OF A TACTICAL GRADE EGI SYSTEM FOR LAND VEHICULAR NAVIGATION APPLICATIONS","year":2023,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"GNSS applications; Computer science; Real-time computing; Inertial measurement unit; Sensor fusion; GNSS augmentation; Process (computing); Global Positioning System; Navigation system; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001196708,0.0004562937,0.0003144674,0.0006981505,0.0003816763,0.000722247,0.001000208,0.0006417563,0.001680736],"category_scores_gemma":[0.001148244,0.0001240477,0.0002276538,0.0004554665,0.0003404985,0.0005130605,0.0006584724,0.0003974201,0.00117576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006718747,"about_ca_system_score_gemma":0.001168298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004813259,"about_ca_topic_score_gemma":0.004977887,"domain_scores_codex":[0.9994155,0.00008505126,0.00002606763,0.0001003691,0.0003229948,0.00005001498],"domain_scores_gemma":[0.9994629,0.00004174999,0.00003416363,0.0001386958,0.0002774837,0.00004495901],"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":[0.0007035501,0.0005069198,0.03457681,0.000484236,0.0001995517,0.0006551551,0.0009235187,0.0683816,0.4578349,0.005822199,0.006374527,0.423537],"study_design_scores_gemma":[0.000249681,0.002730651,0.07261666,0.0001378814,0.0002836794,0.0006213553,0.0004839657,0.3294061,0.5076067,0.001245838,0.08449027,0.0001270464],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4762534,0.0003865357,0.5001732,0.0001774293,0.0002342828,0.0009747598,0.001574153,0.005822131,0.01440411],"genre_scores_gemma":[0.8202179,0.000124623,0.1711542,0.0001207599,0.00001534662,0.0002971881,0.002923101,0.0001655761,0.004981348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004813259,"threshold_uncertainty_score":0.009570479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01750413533296473,"score_gpt":0.2566225663684807,"score_spread":0.239118431035516,"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."}}