{"id":"W2560459036","doi":"10.14722/ndss.2016.23407","title":"What Mobile Ads Know About Mobile Users","year":2016,"lang":"en","type":"article","venue":"","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Mobile telephony; Mobile computing; Internet privacy; Mobile radio; Telecommunications","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":[0.0005062025,0.0003289488,0.000429293,0.002482914,0.0005310273,0.002559296,0.0003268397,0.001152606,0.005321641],"category_scores_gemma":[0.01175187,0.0003058886,0.000349038,0.001808682,0.0004859192,0.004316331,0.0004627531,0.0009193571,0.002418973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005317313,"about_ca_system_score_gemma":0.0003469265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01019213,"about_ca_topic_score_gemma":0.0083497,"domain_scores_codex":[0.9991477,0.0001717986,0.0000731289,0.0001722668,0.0003114442,0.0001236982],"domain_scores_gemma":[0.9870402,0.006665568,0.002900465,0.0007336587,0.001955364,0.0007047487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002938144,0.000191697,0.8860765,0.0003942047,0.0001587094,0.0004207578,0.003243831,0.0006706045,0.002018411,0.002046528,0.007355988,0.09712894],"study_design_scores_gemma":[0.00002021034,0.000230285,0.9263224,0.0003424088,0.0003679748,0.003219406,0.007372999,0.006937443,0.004183169,0.005401938,0.04549478,0.0001070776],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9439756,0.005694206,0.001873718,0.006116191,0.00009632263,0.00003684898,0.007719529,0.0001797162,0.03430788],"genre_scores_gemma":[0.9922781,0.001796457,0.0005120239,0.0007068941,0.0002536138,0.000009275152,0.001703021,0.0000294757,0.002711119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01019213,"threshold_uncertainty_score":0.02026558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009135491329581665,"score_gpt":0.2383592746906329,"score_spread":0.2292237833610513,"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."}}