{"id":"W4256707813","doi":"10.32920/ryerson.14647236.v1","title":"An integrated positioning system for the Toronto Transit System","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Global Positioning System; Hybrid positioning system; Positioning system; Dead reckoning; Transit (satellite); Kalman filter; Precision Lightweight GPS Receiver; Positioning technology; Computer science; Automatic vehicle location; Systems design; Real-time computing; Navigation system; Assisted GPS; System integration; Engineering; Telecommunications; Systems engineering; Transport engineering; Public transport; Node (physics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003317008,0.0007540099,0.0004461452,0.0007740688,0.0007730821,0.001010952,0.0009565421,0.0004877743,0.01351234],"category_scores_gemma":[0.0006347951,0.0002220834,0.0002620886,0.001459546,0.0002515671,0.000387564,0.0007728254,0.0005467135,0.005285742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002779346,"about_ca_system_score_gemma":0.004447837,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1976335,"about_ca_topic_score_gemma":0.2387405,"domain_scores_codex":[0.9992616,0.00005767132,0.00002080379,0.0001456356,0.0004395874,0.00007476709],"domain_scores_gemma":[0.9994928,0.00002023996,0.00003033259,0.00005390547,0.0003558518,0.00004684438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009541342,0.0001397079,0.01578569,0.000780844,0.0001898452,0.0008016262,0.001303109,0.1214446,0.08966336,0.02014358,0.1162896,0.632504],"study_design_scores_gemma":[0.0002794332,0.0008573727,0.04138561,0.0001316121,0.0003432067,0.0005631252,0.0004801201,0.5859926,0.03197196,0.001673602,0.3360883,0.0002329699],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.087526,0.001616159,0.8056955,0.0005195374,0.0006621427,0.0006984316,0.01036574,0.02318809,0.06972832],"genre_scores_gemma":[0.7322489,0.0009129301,0.1895252,0.0001368902,0.0001750324,0.0005331307,0.01696502,0.0003332243,0.05916964],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8023665,"threshold_uncertainty_score":0.3929663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02028697836900178,"score_gpt":0.2958471246567963,"score_spread":0.2755601462877945,"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."}}