{"id":"W3081693698","doi":"","title":"Chaperone: Real-time Locking and Loss Prevention for Smartphones","year":2020,"lang":"en","type":"article","venue":"USENIX Security Symposium","topic":"Green IT and Sustainability","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of Waterloo","funders":"","keywords":"Computer science; Chaperone (clinical); Real-time computing; Medicine","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":[0.001319586,0.00170286,0.001033457,0.001208874,0.000918709,0.002112522,0.002643058,0.0009966771,0.02081489],"category_scores_gemma":[0.006042333,0.0006923361,0.0004381926,0.0005749332,0.0007341211,0.002248283,0.003484701,0.001660946,0.007553759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004603544,"about_ca_system_score_gemma":0.001384955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002135436,"about_ca_topic_score_gemma":0.003320936,"domain_scores_codex":[0.9985936,0.0001642076,0.00008888724,0.000228389,0.0006307841,0.0002940924],"domain_scores_gemma":[0.9962643,0.0006800828,0.0003544393,0.001343164,0.0008761704,0.0004819978],"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":[0.002947515,0.0008659203,0.0117469,0.0008827788,0.0001522354,0.001057184,0.0009758712,0.006203426,0.0720883,0.005861138,0.1978584,0.6993603],"study_design_scores_gemma":[0.001100541,0.004821379,0.02420807,0.0008084095,0.0006771629,0.003949644,0.001577233,0.405031,0.2309804,0.01180391,0.3143255,0.0007167106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1583589,0.005198683,0.4642781,0.002612279,0.002883671,0.001073609,0.002049214,0.3272081,0.03633732],"genre_scores_gemma":[0.8629834,0.001441722,0.07250834,0.001507346,0.000383451,0.0003884488,0.001672532,0.005404822,0.05370998],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02081489,"threshold_uncertainty_score":0.06963277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007656006918390076,"score_gpt":0.2092686331551266,"score_spread":0.2016126262367365,"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."}}