{"id":"W2971109861","doi":"10.1109/tii.2019.2938248","title":"Gender Profiling From a Single Snapshot of Apps Installed on a Smartphone: An Empirical Study","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Digital Communication and Language","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"Zhejiang University; State Key Laboratory of Computer Aided Design and Computer Graphics; China Postdoctoral Science Foundation; National Natural Science Foundation of China; Canada Foundation for Innovation","keywords":"Snapshot (computer storage); Android (operating system); Computer science; Profiling (computer programming); Android app; Empirical research; Mobile apps; Inference; World Wide Web; Data science; Artificial intelligence; Database","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001778214,0.0003517027,0.0003941936,0.001167521,0.0005262069,0.000840474,0.0006757533,0.0005757133,0.001626053],"category_scores_gemma":[0.01098173,0.0002492045,0.0003504632,0.001422243,0.0004793984,0.00130565,0.0007296226,0.0008102098,0.00122429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003397767,"about_ca_system_score_gemma":0.0003972014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007230939,"about_ca_topic_score_gemma":0.01108606,"domain_scores_codex":[0.9988079,0.0004120475,0.0001025232,0.0002302527,0.0003142032,0.0001330415],"domain_scores_gemma":[0.9887683,0.005150149,0.002306913,0.0009326319,0.002208023,0.0006339499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001800597,0.0003898186,0.9700171,0.0001143018,0.00004291517,0.000403443,0.004947782,0.0001812121,0.0007537059,0.0001929138,0.002728445,0.02004838],"study_design_scores_gemma":[0.000008345715,0.0001850913,0.9840492,0.00006119056,0.00003781861,0.0005340656,0.007858133,0.003264345,0.0005660228,0.0001129154,0.003290871,0.00003200231],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975018,0.0001271216,0.0003673229,0.00008412328,0.000007669353,0.00003157843,0.001385989,0.000007554953,0.0004868357],"genre_scores_gemma":[0.9952494,0.0002249872,0.001042995,0.000114322,0.00002917643,0.00007329226,0.002574808,0.00001062057,0.0006804489],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007230939,"threshold_uncertainty_score":0.01437771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1196332769463697,"score_gpt":0.3122562851993131,"score_spread":0.1926230082529435,"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."}}