{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002840061,0.0001738002,0.0002654862,0.0002229954,0.000087924,0.0001860479,0.000806466,0.0001495636,0.0001308626],"category_scores_gemma":[0.00001421171,0.0001541411,0.0000909675,0.0004836938,0.00003345456,0.000969333,0.00001173932,0.0004813091,0.00009757105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006781352,"about_ca_system_score_gemma":0.0001415085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003899056,"about_ca_topic_score_gemma":0.00001430303,"domain_scores_codex":[0.9984121,0.0001198678,0.0006639289,0.0001553083,0.0004599541,0.0001887789],"domain_scores_gemma":[0.998266,0.0002096844,0.0002070175,0.001127971,0.00007359043,0.0001156748],"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":[0.003701544,0.05771131,0.005444863,0.0002084666,0.001926207,0.00002959412,0.2701852,0.1612401,0.006454132,0.009007406,0.001825069,0.4822662],"study_design_scores_gemma":[0.04712752,0.0284105,0.001777407,0.0006386843,0.0003230151,0.00002908841,0.1145251,0.5556428,0.2339588,0.001462101,0.01206862,0.004036314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8633724,0.000001913406,0.1095828,0.0000361046,0.0004299151,0.0007103424,0.00003738449,0.000109157,0.02571995],"genre_scores_gemma":[0.995587,0.000001026316,0.003982113,0.0002978902,0.00001905425,0.00002336357,0.00002088521,0.000009789839,0.00005890979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4782299,"threshold_uncertainty_score":0.6285692,"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."}}