{"id":"W2312023463","doi":"10.5194/isprsarchives-xl-1-w4-75-2015","title":"A NEW SURVEY ON SELF-TUNING INTEGRATED LOW-COST GPS/INS VEHICLE NAVIGATION SYSTEM IN HARSH ENVIRONMENT","year":2015,"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":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Global Positioning System; Inertial navigation system; Inertial measurement unit; Extended Kalman filter; Robustness (evolution); Computer science; GPS/INS; Navigation system; Kalman filter; Real-time computing; Assisted GPS; Artificial intelligence; Inertial frame of reference; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001624674,0.0004069424,0.0003978954,0.0008287794,0.0006065221,0.0005361139,0.001085127,0.0001084249,0.000003394884],"category_scores_gemma":[0.0004096815,0.000275303,0.0002181705,0.0009746224,0.0009081546,0.0004927389,0.0003870076,0.0004991149,0.000006616266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001839972,"about_ca_system_score_gemma":0.0002094762,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8397372,"about_ca_topic_score_gemma":0.1602476,"domain_scores_codex":[0.9958471,0.0003376243,0.001354911,0.0003333055,0.001650861,0.0004761884],"domain_scores_gemma":[0.9976244,0.0007149373,0.0008749737,0.0003825733,0.0002008906,0.0002022228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001713479,0.00001737219,0.0004554347,0.00004009193,0.00005662895,5.45142e-7,0.003980862,0.05311368,0.001801165,0.000006370839,0.00006214768,0.9402943],"study_design_scores_gemma":[0.001003083,0.0001138514,0.007088914,0.0005778265,0.0000251501,0.00005929586,0.001770791,0.9763948,0.01024787,0.0008749968,0.001545673,0.0002977494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07663633,0.00002158011,0.9151266,0.0006954051,0.001819414,0.00076421,0.0001193781,0.0000976606,0.004719473],"genre_scores_gemma":[0.9973469,0.000064554,0.002048776,0.0002475205,0.0001221185,4.995139e-7,0.0001141406,0.00001582234,0.00003969988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9399966,"threshold_uncertainty_score":0.9999699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01861880488018594,"score_gpt":0.2359300992714123,"score_spread":0.2173112943912264,"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."}}