{"id":"W2915763604","doi":"10.1109/glocom.2018.8647989","title":"Reliability-Driven Vehicular Crowd-Sensing: A Case Study for Localization in Public Transportation","year":2018,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Reliability (semiconductor); Trustworthiness; Computer science; Global Positioning System; Identification (biology); Cloud computing; Crowdsourcing; Public transport; Key (lock); Reliability engineering; Real-time computing; Data mining; Computer security; Transport engineering; Engineering","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":[0.002379001,0.0006191478,0.000647088,0.0007048102,0.001500102,0.001032044,0.001665634,0.002074242,0.0009126759],"category_scores_gemma":[0.005919854,0.0002605225,0.0006602209,0.001044544,0.001512086,0.001424767,0.001766208,0.0007065528,0.0001904952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001396063,"about_ca_system_score_gemma":0.001182984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01326047,"about_ca_topic_score_gemma":0.01080954,"domain_scores_codex":[0.9978568,0.001199609,0.00008136427,0.0002368259,0.0003460553,0.0002793825],"domain_scores_gemma":[0.9953596,0.003078632,0.0003850745,0.0004393196,0.0004542532,0.0002831897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0007666986,0.0003999445,0.03970364,0.0005520732,0.0001981052,0.01159172,0.00443696,0.8211091,0.008532248,0.04856846,0.005277672,0.05886343],"study_design_scores_gemma":[0.00009516961,0.0004173346,0.007517818,0.00007073593,0.00009627326,0.002280239,0.005506535,0.941569,0.008329676,0.02117169,0.01284217,0.0001032491],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6884615,0.0009240582,0.2988364,0.002187155,0.000126237,0.0004112193,0.0003965806,0.000476663,0.008180204],"genre_scores_gemma":[0.9829722,0.0001616658,0.01583034,0.00004390772,0.00002134578,0.00007130591,0.0000793343,0.00001591827,0.0008040005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01326047,"threshold_uncertainty_score":0.02636653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02690272375727416,"score_gpt":0.2706399724270319,"score_spread":0.2437372486697577,"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."}}