{"id":"W3036094796","doi":"10.1007/1345_2020_120","title":"Assessment of a GNSS/INS/Wi-Fi Tight-Integration Method Using Support Vector Machine and Extended Kalman Filter","year":2020,"lang":"en","type":"book-chapter","venue":"International Association of Geodesy symposia","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"GNSS applications; Computer science; Kalman filter; Extended Kalman filter; Inertial navigation system; Context (archaeology); Kinematics; Real-time computing; Global Positioning System; Artificial intelligence; Inertial frame of reference; Telecommunications; Geography","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":[0.001739149,0.0007237197,0.0007022691,0.000874072,0.0003289083,0.0007285085,0.0005156228,0.00082996,0.001362794],"category_scores_gemma":[0.003617572,0.0002739026,0.0004751839,0.0005472187,0.0002595138,0.0007302747,0.0007565107,0.0006051138,0.0005944748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003580913,"about_ca_system_score_gemma":0.0008390453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008288685,"about_ca_topic_score_gemma":0.004126462,"domain_scores_codex":[0.9992815,0.0001871358,0.00004974438,0.0001483697,0.0002480026,0.00008521091],"domain_scores_gemma":[0.9990271,0.0002908345,0.00009374881,0.00008102203,0.0004593386,0.00004790386],"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":[0.0006871756,0.0003018951,0.01320727,0.0002008611,0.0001877976,0.0001606115,0.0001421432,0.4921735,0.02112655,0.002029213,0.001188416,0.4685946],"study_design_scores_gemma":[0.000006977005,0.00005797806,0.002065019,0.000007462696,0.00001436148,0.00001370905,0.0000205058,0.9951991,0.002131037,0.0001675401,0.0003103225,0.000006072779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1764818,0.0003699976,0.8191384,0.0001498392,0.0001168279,0.00006689406,0.00007012116,0.001543576,0.002062529],"genre_scores_gemma":[0.8391566,0.0001048586,0.1590677,0.00003512646,0.0000217196,0.00004664448,0.000150997,0.0000640051,0.001352451],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008288685,"threshold_uncertainty_score":0.01648086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01218022775668416,"score_gpt":0.2690081239341924,"score_spread":0.2568278961775083,"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."}}