{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006654342,0.0004085252,0.0004522699,0.0006843705,0.0007637498,0.0003810856,0.0006663805,0.0002091038,0.00005112995],"category_scores_gemma":[0.00001286474,0.0004505156,0.0003561371,0.00121583,0.00004106756,0.0004754549,0.000008528215,0.000702877,0.0001325027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003627905,"about_ca_system_score_gemma":0.0001584509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008774897,"about_ca_topic_score_gemma":0.00002803364,"domain_scores_codex":[0.99696,0.0004194,0.0006374102,0.0009764221,0.0004886168,0.0005180839],"domain_scores_gemma":[0.9972062,0.001304928,0.0002241812,0.0008961341,0.000221357,0.0001472211],"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":[0.00007198815,0.0006990126,0.00004955185,0.00008704285,0.00009134416,0.00002170548,0.0005334295,0.4990434,0.08585823,0.00005844004,0.0000913746,0.4133945],"study_design_scores_gemma":[0.0009103821,0.0004378123,0.00004125118,0.0003135799,0.00003016407,0.000006476394,0.0001327378,0.6052427,0.3897798,0.00001961633,0.002734843,0.0003506863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.137502,0.0000649724,0.8577394,0.00005351001,0.002190414,0.001173513,0.00001031769,0.0007675129,0.0004983173],"genre_scores_gemma":[0.996918,0.000002072739,0.001842611,0.0003517463,0.0001081586,0.000350513,0.000002043917,0.00003624304,0.0003886434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8594159,"threshold_uncertainty_score":0.9997947,"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."}}