{"id":"W2960126575","doi":"10.1109/compsac.2019.00120","title":"CSKES: A Context-Based Secure Keyless Entry System","year":2019,"lang":"en","type":"article","venue":"","topic":"RFID technology advancements","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Mitacs","keywords":"Relay; Computer science; Key (lock); Context (archaeology); Global Positioning System; Wireless; Radio-frequency identification; Computer network; Identification (biology); Real-time computing; Computer security; Embedded system; Telecommunications","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00003942219,0.0001201074,0.0001503246,0.00006746611,0.0000158613,0.000009883817,0.0001645967,0.0001206256,0.0002483864],"category_scores_gemma":[0.00000395716,0.0001138102,0.00003196716,0.0001064881,0.00001469531,0.00007006941,0.00001734988,0.0001326094,0.001318833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001002515,"about_ca_system_score_gemma":0.000007966465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004476029,"about_ca_topic_score_gemma":0.000008604769,"domain_scores_codex":[0.9994115,0.000006976682,0.0001368129,0.000140161,0.00009342458,0.0002111568],"domain_scores_gemma":[0.9995858,0.00002289868,0.00001658153,0.0003238904,0.00001804245,0.00003275383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001053844,0.0003273601,0.2221497,0.007664633,0.001102249,0.0002018699,0.0006934988,0.174936,0.1244238,0.380253,0.04283149,0.04531102],"study_design_scores_gemma":[0.007522855,0.0001927973,0.00364116,0.0009268603,0.0000607332,0.00003870228,0.004278493,0.5584323,0.2841832,0.0002654843,0.1388852,0.001572288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8834847,0.0004454342,0.03127888,0.00007906729,0.001045711,0.0005022767,0.00001200934,0.003776711,0.07937517],"genre_scores_gemma":[0.998019,0.000003011884,0.0008555072,0.00007971464,0.00001489057,0.00001918309,0.000005790247,0.00002776869,0.0009751823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3834963,"threshold_uncertainty_score":0.9994587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002350299937225231,"score_gpt":0.1664740983538286,"score_spread":0.1641237984166034,"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."}}