{"id":"W4286377586","doi":"10.1109/comst.2022.3192978","title":"A Survey on Trust Models in Heterogeneous Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Access Control and Trust","field":"Social Sciences","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Higher Education Discipline Innovation Project; Huawei Technologies; National Natural Science Foundation of China","keywords":"Heterogeneous network; Computer science; Exploit; Trust management (information system); Merge (version control); Reliability (semiconductor); Openness to experience; Computer security; Wireless network; Telecommunications; Wireless","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.01678451,0.0001690504,0.000375294,0.0001834629,0.002041719,0.0001489424,0.002504852,0.000111355,0.0002392811],"category_scores_gemma":[0.0006162956,0.0001913743,0.0001025333,0.0010239,0.0003239356,0.0002406329,0.0004201885,0.0005242366,0.00002774067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004156004,"about_ca_system_score_gemma":0.0003240676,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06262829,"about_ca_topic_score_gemma":0.1065963,"domain_scores_codex":[0.9785627,0.01956629,0.0005427018,0.0003126956,0.0005471433,0.0004684647],"domain_scores_gemma":[0.994365,0.003428505,0.0002274644,0.001691532,0.0001674908,0.0001199815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002732365,0.002419943,0.09153495,0.000005615395,0.000198891,0.00002518065,0.01440092,0.7598771,0.00004036149,0.07849459,0.008411447,0.04431769],"study_design_scores_gemma":[0.007817606,0.0007055957,0.3923205,0.00008655027,0.0001334865,0.000006545431,0.005127166,0.174695,0.00004105367,0.03518929,0.3803435,0.003533684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7234545,0.01197717,0.01564993,0.009260288,0.03258999,0.00729122,0.002535978,0.001423663,0.1958172],"genre_scores_gemma":[0.9975461,0.0005732871,0.00005206958,0.0002171091,0.0003753887,0.0003920844,0.0001620385,0.00002727534,0.0006547002],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5851821,"threshold_uncertainty_score":0.9992575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1137661808345762,"score_gpt":0.3523608606094925,"score_spread":0.2385946797749163,"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."}}