{"id":"W4220995962","doi":"10.1155/2022/4463621","title":"Vehicle Routing Optimization Based on Multimedia Communication and Intelligent Transportation System","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Technologies and Applied Computing","field":"Computer Science","cited_by":6,"is_retracted":true,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Intelligent transportation system; Advanced Traffic Management System; Computer science; Multimedia; License; Communications system; Vehicular communication systems; Popularity; Computer network; Vehicular ad hoc network; Telecommunications; Transport engineering; Wireless ad hoc network; Engineering; Wireless","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":{"nature":"Retraction","reason":"Concerns/Issues about Data;Concerns/Issues about Results and/or Conclusions;Concerns/Issues about Referencing/Attributions;Concerns/Issues about Peer Review;Investigation by Journal/Publisher;Investigation by Third Party;Paper Mill;Computer-Aided Content or Computer-Generated Content;Unreliable Results and/or Conclusions;","date":"8/9/2023 0:00","openalex_flagged":true},"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005092684,0.001133914,0.00116176,0.001450114,0.0007131134,0.001282082,0.001095622,0.00119424,0.003322058],"category_scores_gemma":[0.00109396,0.0004489622,0.0007612134,0.001255439,0.0004879624,0.001287838,0.0006522214,0.0005812268,0.0002724417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001764044,"about_ca_system_score_gemma":0.001739633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01429959,"about_ca_topic_score_gemma":0.007296856,"domain_scores_codex":[0.9995857,0.00009846911,0.00002061672,0.0001083668,0.00008387457,0.0001030077],"domain_scores_gemma":[0.9997101,0.0001153467,0.00004437726,0.00001011075,0.00009824666,0.00002187167],"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":[0.00002648303,0.00001590881,0.0002922668,0.00004297611,0.00002061696,0.0000416056,0.00001915542,0.9806805,0.0004785208,0.004256151,0.0008199585,0.01330577],"study_design_scores_gemma":[0.000004434342,0.00001571005,0.0000748144,0.000002864796,0.000007189662,0.000008753385,0.000009934865,0.9980323,0.00009429661,0.001391463,0.0003545974,0.000003731076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05137651,0.00126952,0.93111,0.0007594168,0.000151032,0.0001301662,0.0001649356,0.0003594728,0.01467898],"genre_scores_gemma":[0.9011827,0.0008872414,0.08733361,0.0001647879,0.00009400173,0.0002488309,0.0003597264,0.00006410361,0.00966506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01429959,"threshold_uncertainty_score":0.02843273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009689499495780574,"score_gpt":0.2294399552758319,"score_spread":0.2197504557800514,"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."}}