{"id":"W4411346613","doi":"10.1155/atr/5528500","title":"A Distributed Magnetic Sensor Network: Vehicle Trajectory Tracking Based on Cellular Automaton","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Cellular Automata and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Trajectory; Tracking (education); Cellular automaton; Computer science; Automaton; Vehicle tracking system; Real-time computing; Artificial intelligence; Physics; Kalman filter","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":[0.0002173,0.0004983394,0.0004743693,0.0005004835,0.0004119297,0.0005611409,0.0009312716,0.0004784477,0.000803289],"category_scores_gemma":[0.001016646,0.0001783631,0.0003295904,0.000628562,0.0003557371,0.0006472105,0.0005174695,0.0004089401,0.0003009427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007655388,"about_ca_system_score_gemma":0.0005486013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006784098,"about_ca_topic_score_gemma":0.00569002,"domain_scores_codex":[0.9996903,0.00007267699,0.00001404814,0.0001176745,0.00008341658,0.00002187124],"domain_scores_gemma":[0.9996234,0.0001320061,0.00005940843,0.00004826109,0.0001127102,0.00002408938],"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":[0.0002418896,0.0000881993,0.004195298,0.0001450212,0.00008424307,0.0002159351,0.0001313596,0.7863725,0.02293008,0.01968735,0.002379225,0.1635289],"study_design_scores_gemma":[0.000004800161,0.00003045849,0.0002423376,0.000003723841,0.000007340916,0.0000365686,0.000008560493,0.9957703,0.001303988,0.001492588,0.001093269,0.000005986786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03214477,0.0003886499,0.9634313,0.0001837798,0.0001045643,0.00003969699,0.0001142455,0.0008538551,0.002739175],"genre_scores_gemma":[0.8935962,0.0004156113,0.1024691,0.0000766817,0.00004778629,0.00011285,0.000191161,0.00002579284,0.003064835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006784098,"threshold_uncertainty_score":0.01348925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0068592824292788,"score_gpt":0.2324245178499922,"score_spread":0.2255652354207134,"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."}}