{"id":"W4407900133","doi":"10.1109/tmc.2025.3545437","title":"MoCo: Urban User Mobile Contact Detection Based on Cellular Signaling Trace","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Calgary","funders":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Computer science; TRACE (psycholinguistics); Mobile computing; Cellular network; Computer network; Human–computer interaction","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":[0.0002288182,0.0007722442,0.0005761053,0.0017513,0.0003683722,0.0006182324,0.0008452986,0.00067721,0.001263893],"category_scores_gemma":[0.00129943,0.0001671447,0.0002321517,0.001349315,0.0002304256,0.0008208315,0.001043751,0.0003835175,0.0009745223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004163565,"about_ca_system_score_gemma":0.0005438448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005487158,"about_ca_topic_score_gemma":0.009773169,"domain_scores_codex":[0.9995769,0.00005837912,0.00001713175,0.0001017824,0.0001760409,0.00006983069],"domain_scores_gemma":[0.9994922,0.00007758438,0.00008213345,0.0001005749,0.0001830328,0.00006458143],"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":[0.000992157,0.0006583989,0.1059037,0.0003768934,0.000181942,0.001322467,0.0005700074,0.07316437,0.1015716,0.01061233,0.01893644,0.6857097],"study_design_scores_gemma":[0.00002800448,0.0002034783,0.0336585,0.00002293013,0.00003156741,0.0007235827,0.0002676434,0.9227872,0.03011552,0.002563284,0.009551943,0.00004634633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3187728,0.0003308103,0.659113,0.0002868818,0.00008478243,0.0004422554,0.002709717,0.008212645,0.01004711],"genre_scores_gemma":[0.8817685,0.0001818519,0.1093253,0.000112087,0.00004761354,0.0002418933,0.003094275,0.0001016301,0.00512674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005487158,"threshold_uncertainty_score":0.01091039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01287925799935177,"score_gpt":0.2456706370461345,"score_spread":0.2327913790467827,"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."}}