{"id":"W4394603794","doi":"10.1016/j.asoc.2024.111591","title":"A three-way trajectory privacy-preserving model based on multi-feature fusion","year":2024,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Major Discipline Academic and Technical Leaders Training Program of Jiangxi Province; China Scholarship Council; Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Computer science; Feature (linguistics); Trajectory; Fusion; Privacy protection; Artificial intelligence; Data mining; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0009803342,0.0005110333,0.0003937116,0.0004786051,0.0004679339,0.0006821157,0.02544081,0.0003802308,0.00001034416],"category_scores_gemma":[0.002798715,0.0004828311,0.0001634288,0.001223304,0.0001059493,0.0004137557,0.0594099,0.001326488,0.000113212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002351792,"about_ca_system_score_gemma":0.0001989833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001852623,"about_ca_topic_score_gemma":0.00001110578,"domain_scores_codex":[0.9962034,0.00004875666,0.0004323293,0.001650184,0.0007669842,0.0008983177],"domain_scores_gemma":[0.9898779,0.0008877147,0.0001367798,0.008889074,0.00006335327,0.0001451426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004892901,0.0003841535,0.0002701452,0.0006415585,0.0001130059,0.0002195095,0.001007565,0.1882223,0.02111218,0.0234,0.250323,0.5142577],"study_design_scores_gemma":[0.0004142813,0.00003730924,0.0002342693,0.000345138,0.00001135774,0.000006942995,0.00001542449,0.9501727,0.001774292,0.04561771,0.0008639084,0.0005067106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008286381,0.0003563831,0.9747115,0.007579799,0.0006161419,0.0005207265,0.00001287875,0.006190297,0.001725927],"genre_scores_gemma":[0.4722437,0.000003338678,0.5270875,0.0004898532,0.00007848266,0.00002031526,0.00001401404,0.00004603845,0.00001677871],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7619504,"threshold_uncertainty_score":0.9997624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03239570142681369,"score_gpt":0.2715701892781843,"score_spread":0.2391744878513706,"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."}}