{"id":"W3035776698","doi":"10.1155/2020/6026140","title":"Privacy Protection Method for Vehicle Trajectory Based on VLPR Data","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Trajectory; Computer science; Generalization; Adversary; License; Tracking (education); Information privacy; Computer security; Privacy protection; Data Protection Act 1998; Data mining; Mathematics","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":[0.0009793018,0.00007387814,0.0001724419,0.00008060034,0.0002336654,0.00002448293,0.0002624237,0.00005475126,0.00006480801],"category_scores_gemma":[0.0004689606,0.00007131494,0.0001228711,0.0003090317,0.00003670187,0.000482817,9.373341e-7,0.0001558663,0.000001867405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000654285,"about_ca_system_score_gemma":0.0002879254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001110091,"about_ca_topic_score_gemma":0.000884752,"domain_scores_codex":[0.9987749,0.0001537164,0.0003944433,0.0001831263,0.0003765406,0.0001172217],"domain_scores_gemma":[0.9988929,0.0002200973,0.0003469976,0.0001481219,0.0002761414,0.0001157166],"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":[0.001650974,0.0003389001,0.0006839571,0.0001795073,0.0000897506,0.000005936632,0.02732219,0.691744,0.01368255,0.0008096586,0.0002375324,0.263255],"study_design_scores_gemma":[0.01399502,0.005299374,0.1311291,0.0005286424,0.001621558,8.561353e-7,0.03293964,0.4799786,0.01745792,0.008431362,0.3072943,0.001323607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0707591,0.00003868323,0.9198595,0.008603787,0.0001396113,0.0004643147,0.00004401187,0.0000292371,0.00006169736],"genre_scores_gemma":[0.9606467,0.00001534704,0.03839033,0.0005063099,0.0003386973,0.00001301772,0.0000672177,0.000009358218,0.00001302806],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8898876,"threshold_uncertainty_score":0.2908139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06972386542386812,"score_gpt":0.3641095276884175,"score_spread":0.2943856622645494,"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."}}