{"id":"W4214948608","doi":"10.1155/2022/3815306","title":"A Practical and Economical Ultra-wideband Base Station Placement Approach for Indoor Autonomous Driving Systems","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":268,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality; Shanghai Municipal Education Commission","keywords":"Multilateration; Base station; Software deployment; Computer science; Real-time computing; Ultra-wideband; Dilution of precision; Base (topology); Real-time locating system; Wideband; Simulation; Engineering; Electronic engineering; Telecommunications; Global Positioning System","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002407414,0.00009531437,0.0001831695,0.0001401551,0.0001004306,0.0000263248,0.00005106487,0.00004039853,0.000007660537],"category_scores_gemma":[0.00003395402,0.00009749835,0.00004773492,0.00007976339,0.00001755546,0.0003356003,0.000001471601,0.0001846171,1.16105e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001521051,"about_ca_system_score_gemma":0.00004617262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001246872,"about_ca_topic_score_gemma":0.00000302523,"domain_scores_codex":[0.9991584,0.00001915589,0.0004642566,0.00009772673,0.0001354951,0.0001249208],"domain_scores_gemma":[0.9995457,0.0001030692,0.0001880088,0.00005440871,0.00006462483,0.00004419727],"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":[0.0001133483,0.00003580637,0.0006436588,0.0001031325,0.00003807202,0.000005170004,0.0006759044,0.9915992,0.003856497,0.001589808,0.0001409647,0.001198417],"study_design_scores_gemma":[0.01689404,0.002880448,0.01588779,0.0001536429,0.000590153,0.0004587458,0.05609833,0.831198,0.05480504,0.00211165,0.01750301,0.001419182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2913406,0.0001431816,0.7077949,0.00006591928,0.0002519522,0.0002837963,0.00003641715,0.00005791477,0.00002536476],"genre_scores_gemma":[0.9715144,0.00006234977,0.02819535,0.00001386991,0.00003920068,0.00007136174,0.0000756016,0.00001979,0.00000806898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6801739,"threshold_uncertainty_score":0.3975867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01195070939090359,"score_gpt":0.2381296605792647,"score_spread":0.2261789511883611,"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."}}