{"id":"W2770032936","doi":"10.1109/vtcspring.2017.8108298","title":"GoSense: Efficient Vehicle Selection for User Defined Vehicular Crowdsensing","year":2017,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Crowdsensing; Computer science; Vehicular ad hoc network; Scale (ratio); Cloud computing; Scheme (mathematics); Crowdsourcing; Set (abstract data type); Wireless ad hoc network; Participatory sensing; Real-time computing; Computer security; Data science; Telecommunications; Wireless; World Wide Web","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":[0.0006389371,0.0008358127,0.001233307,0.0008113647,0.000822716,0.0007347658,0.00182127,0.0007585959,0.001344321],"category_scores_gemma":[0.001684887,0.0002730932,0.0004600447,0.0006236242,0.0005670546,0.0007552951,0.00274287,0.0005136555,0.0006201548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006370012,"about_ca_system_score_gemma":0.000972897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003276958,"about_ca_topic_score_gemma":0.004981794,"domain_scores_codex":[0.9994097,0.0001276073,0.00002685276,0.0001059172,0.0002191363,0.000110951],"domain_scores_gemma":[0.999496,0.0001712388,0.00004939782,0.00006663273,0.000101202,0.0001154827],"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":[0.001741185,0.000511826,0.00659008,0.0004446552,0.0001917076,0.001815089,0.0008533635,0.5097021,0.07050741,0.02315812,0.02410408,0.3603804],"study_design_scores_gemma":[0.00006505394,0.0001234815,0.000406004,0.00000753074,0.00001552089,0.0001704095,0.0001453422,0.9838697,0.005341432,0.005119446,0.004713514,0.0000224841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09195827,0.0007723863,0.8944135,0.0004527432,0.0003021994,0.0004600611,0.0003798644,0.005289882,0.005971118],"genre_scores_gemma":[0.8963391,0.000205726,0.09888531,0.0001553431,0.00009434121,0.0002039746,0.0004683255,0.0001292676,0.003518603],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003276958,"threshold_uncertainty_score":0.006515741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01703466824964611,"score_gpt":0.2550698172370038,"score_spread":0.2380351489873577,"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."}}