{"id":"W4376270101","doi":"10.1155/2023/8668473","title":"Vehicular Crowdsensing with High-Mileage Vehicles: Investigating Spatiotemporal Coverage Dynamics in Historical Cities with Complex Urban Road Networks","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Taxis; Transport engineering; Computer science; Pedestrian; Grid; Work (physics); Complex network; Geography; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004422929,0.0004155696,0.0002798332,0.0008433974,0.0003875188,0.0006950443,0.000654507,0.0005423121,0.0003553121],"category_scores_gemma":[0.001918063,0.0001707334,0.0003557939,0.000954212,0.000583954,0.0008106048,0.0005561363,0.0003248394,0.00008173853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009741005,"about_ca_system_score_gemma":0.0004360209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02075431,"about_ca_topic_score_gemma":0.01758746,"domain_scores_codex":[0.9997161,0.00006501963,0.0000136976,0.00007004319,0.00006525456,0.0000697604],"domain_scores_gemma":[0.9990124,0.0004854074,0.0001855523,0.0001276085,0.0001196511,0.00006941529],"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":[0.0002445548,0.0001873088,0.1141579,0.0001890922,0.000152687,0.0008056253,0.0003959373,0.8564968,0.005414451,0.002374591,0.001340324,0.01824076],"study_design_scores_gemma":[0.00001283895,0.00006783944,0.04331656,0.00002179208,0.00003054015,0.0001680676,0.0006248622,0.9495252,0.003782853,0.001266096,0.00115689,0.00002654858],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869111,0.0001805497,0.01044079,0.000186476,0.00002945538,0.00003261508,0.0006122002,0.0001001931,0.001506664],"genre_scores_gemma":[0.9978111,0.00005164809,0.001760096,0.000007838405,0.000006827988,0.00001019258,0.0002557126,0.000003885994,0.00009281145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02075431,"threshold_uncertainty_score":0.04126704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01155965085345067,"score_gpt":0.2129315041652617,"score_spread":0.201371853311811,"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."}}