{"id":"W2760626209","doi":"10.48550/arxiv.1709.07198","title":"Cyber Insurance for Heterogeneous Wireless Networks","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer security; Reliability (semiconductor); Service (business); Wireless network; Computer science; Wireless; Business; Risk analysis (engineering); 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002080896,0.0003302796,0.0003848945,0.0001193101,0.0004269691,0.0003416852,0.003031515,0.0003286533,0.000002975832],"category_scores_gemma":[0.00002092104,0.0003858033,0.0004083758,0.0001024716,0.00009852667,0.0003120671,0.001759395,0.0004878887,0.00002394711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001232048,"about_ca_system_score_gemma":0.0001146677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002237339,"about_ca_topic_score_gemma":0.0000798183,"domain_scores_codex":[0.9980808,0.00007477504,0.0001711862,0.001168343,0.00007138825,0.0004335244],"domain_scores_gemma":[0.997182,0.0001108653,0.0003330577,0.002029494,0.0001901018,0.0001544917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009464726,0.0001114284,0.004975057,0.00009310535,0.0002349201,0.0005476866,0.0001025224,0.9302691,0.00003358066,0.05230382,0.0009699311,0.01026415],"study_design_scores_gemma":[0.0005202724,0.00004216976,0.0006266247,0.0001269563,0.00003930856,0.00001087768,0.000004429848,0.9886118,0.00003656432,0.008798709,0.0006765733,0.0005057593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3220745,0.0001885693,0.6751007,0.0000625356,0.001467296,0.0002823013,0.00002537507,0.0002130603,0.0005855674],"genre_scores_gemma":[0.996572,0.0002770683,0.0002602591,0.0001422184,0.0002104029,0.000003587079,0.00001664576,0.00002305615,0.002494738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6748405,"threshold_uncertainty_score":0.9998594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07288304696134598,"score_gpt":0.1904284001815963,"score_spread":0.1175453532202503,"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."}}