{"id":"W3214905160","doi":"10.1145/3462675","title":"Origin-Aware Location Prediction Based on Historical Vehicle Trajectories","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Trajectory; Baseline (sea); Exploit; Markov chain; Data mining; Machine learning; Predictive modelling; Temporal difference learning; Travel time; Artificial intelligence","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":[0.0003547961,0.0007846401,0.0006140794,0.001373314,0.0003523956,0.0006119533,0.001392367,0.0005016051,0.001037042],"category_scores_gemma":[0.002196263,0.0003132686,0.0004784621,0.00206149,0.0002328295,0.002024581,0.0007625271,0.001035041,0.001073386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005236472,"about_ca_system_score_gemma":0.0007760585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02278844,"about_ca_topic_score_gemma":0.03986433,"domain_scores_codex":[0.9997045,0.00003289138,0.00002011197,0.0001309552,0.0000683029,0.00004310164],"domain_scores_gemma":[0.9989941,0.0002497531,0.0001602339,0.00020173,0.000322608,0.00007170424],"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":[0.0003882329,0.0001722559,0.1025198,0.0002040402,0.0001396171,0.0004054227,0.0001925414,0.7111223,0.004262479,0.003669281,0.008994563,0.1679296],"study_design_scores_gemma":[0.000004667089,0.00002040296,0.003522948,0.00001023549,0.00001717494,0.00005956563,0.0000498256,0.9927527,0.001150773,0.001504811,0.0008984064,0.0000086369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.391395,0.00143243,0.59006,0.0006937269,0.0002022718,0.00009946238,0.009252069,0.003467074,0.003398089],"genre_scores_gemma":[0.9404292,0.0005856861,0.04849882,0.000037142,0.00006023374,0.00003439112,0.008818276,0.00006925988,0.001467029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02278844,"threshold_uncertainty_score":0.04531157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02315088312893285,"score_gpt":0.2409021286629922,"score_spread":0.2177512455340594,"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."}}