{"id":"W7002265701","doi":"","title":"Multi-Sensor Based Land Vehicles’ Positioning in Challenging GNSS Environments","year":2020,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"GNSS applications; GNSS augmentation; Inertial measurement unit; Global Positioning System; Hybrid positioning system; Precise Point Positioning; Satellite system; Inertial navigation system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000258206,0.0004179886,0.000396067,0.0003386516,0.0002147251,0.0004640613,0.0006732881,0.000494553,0.0005600031],"category_scores_gemma":[0.0004883982,0.0001857326,0.0001861537,0.0004054816,0.0002118837,0.0006890794,0.0005219223,0.0002435652,0.0003633174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003449387,"about_ca_system_score_gemma":0.0003073839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004283613,"about_ca_topic_score_gemma":0.00584365,"domain_scores_codex":[0.9996899,0.00004565483,0.00001048515,0.00008495931,0.0001334439,0.00003551775],"domain_scores_gemma":[0.9998114,0.00002981156,0.00003216655,0.0000321396,0.00007859396,0.00001594925],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000390303,0.0001154709,0.01093207,0.0001921522,0.0001156789,0.0003796424,0.0002874902,0.6042524,0.1712838,0.00214783,0.001075802,0.2088273],"study_design_scores_gemma":[0.00001063811,0.0001834276,0.006910918,0.00001045617,0.00002470059,0.00008773518,0.00008766318,0.9600641,0.03049846,0.0004984619,0.001603512,0.00001987396],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3840819,0.0004671558,0.6089118,0.0001232338,0.00009676144,0.0000667909,0.0001405933,0.001448885,0.004662917],"genre_scores_gemma":[0.940875,0.0001237836,0.05761084,0.00002966905,0.000009318658,0.00001909138,0.0001168867,0.00001998388,0.001195373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004283613,"threshold_uncertainty_score":0.008517325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01008759699579629,"score_gpt":0.2051370956910889,"score_spread":0.1950494986952926,"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."}}