{"id":"W3123172398","doi":"10.1155/2021/1586010","title":"Refining Sparse Cell-ID Trajectory of Public Service Vehicles by Spatiotemporal Modelling","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Heuristic; Precision and recall; Position (finance); Trajectory optimization; Population; Real-time computing; Data mining; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008001942,0.00008230703,0.0002350854,0.0001079371,0.0001871881,0.00002737716,0.0001284422,0.00007510214,0.0001305711],"category_scores_gemma":[0.00004573278,0.00008890478,0.0001309108,0.0005279279,0.00006091805,0.0006131103,0.000001017867,0.0001758265,0.000001447362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007123219,"about_ca_system_score_gemma":0.0005377988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00102575,"about_ca_topic_score_gemma":0.01302907,"domain_scores_codex":[0.9983888,0.0001768352,0.0006503153,0.0001310785,0.0005001686,0.000152863],"domain_scores_gemma":[0.9981399,0.0001287546,0.0006081039,0.00009897487,0.0009156347,0.000108617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","study_design_scores_codex":[0.00008343517,0.0004418957,0.01146778,0.0001769669,0.00009114401,0.00001966183,0.03554372,0.894348,0.03417449,0.0009474457,0.00007280105,0.02263264],"study_design_scores_gemma":[0.01398151,0.001262028,0.1486208,0.001875963,0.002529129,0.000009800271,0.4134261,0.03140427,0.2386602,0.01320608,0.1319815,0.003042721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9603485,0.0008052905,0.03746239,0.0009290506,0.0001107812,0.00003705096,0.00001769489,0.0000113659,0.0002779048],"genre_scores_gemma":[0.9940625,0.0002896412,0.005288864,0.00008979157,0.00008156852,0.00000157805,0.00008131834,0.00000886364,0.0000958887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8629438,"threshold_uncertainty_score":0.7270527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03465576431768253,"score_gpt":0.2794401716137882,"score_spread":0.2447844072961057,"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."}}