{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001625118,0.00009091688,0.0000825004,0.00005987479,0.00007813163,0.0002935455,0.0004975303,0.00005800151,0.0002406166],"category_scores_gemma":[0.00001130221,0.00005695342,0.00005439084,0.0001680706,0.00003792273,0.001999352,0.0001178596,0.00004726108,0.0008092942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003962674,"about_ca_system_score_gemma":0.00002516732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005530588,"about_ca_topic_score_gemma":0.00003045366,"domain_scores_codex":[0.9991801,0.00003055777,0.000114305,0.0002965028,0.0001693515,0.0002091227],"domain_scores_gemma":[0.9992394,0.00008613291,0.00003525989,0.0005048898,0.00004863762,0.00008562942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005616053,0.00005113828,0.0008643612,0.000005600164,0.00001230944,0.000004544816,0.0008995152,0.0000499765,0.009581718,0.009733574,0.008174638,0.970617],"study_design_scores_gemma":[0.0007855626,0.0006649196,0.003347839,0.0001749241,0.00000722607,0.00002031269,0.0001889883,0.004999592,0.08567262,0.004952548,0.8986414,0.0005440686],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3852855,0.002310566,0.5948685,0.001104981,0.007541539,0.0005019888,0.000001540673,0.001320146,0.007065229],"genre_scores_gemma":[0.9858277,0.0005068575,0.003280426,0.0003983873,0.0001557615,0.0001009799,2.238813e-7,0.000009096895,0.009720545],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9700729,"threshold_uncertainty_score":0.9999687,"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."}}