{"id":"W4386025630","doi":"10.1109/tnsm.2023.3307013","title":"Cost-Efficient and Trust-Aware Virtual Network Embedding for Dense Industrial IoT Systems Using Multiagent Systems","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Embedding; Internet of Things; Distributed computing; Computer network; Embedded system; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008523741,0.0003832081,0.0004298867,0.0002123716,0.001073384,0.0005488058,0.0003846427,0.0001985461,0.000002692007],"category_scores_gemma":[0.000001789113,0.0003738161,0.0000992882,0.001404511,0.0000326917,0.0000932121,0.00004390228,0.0002626439,0.00001171672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001155646,"about_ca_system_score_gemma":0.00003223322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001365504,"about_ca_topic_score_gemma":0.00003984564,"domain_scores_codex":[0.9972398,0.0001566182,0.0005266787,0.0008035476,0.0003956124,0.0008777881],"domain_scores_gemma":[0.9985748,0.000405457,0.0001639657,0.0005164404,0.00009382243,0.0002455031],"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":[0.00007747189,0.00005037768,0.00003368058,0.0001492423,0.0001759241,0.0000256688,0.0002848586,0.9678564,0.000001193923,0.001120677,0.001626512,0.02859798],"study_design_scores_gemma":[0.001504124,0.0001487518,0.00006365801,0.000444238,0.0001526484,0.00001851473,0.0006959937,0.9885525,0.000002340812,0.00002575973,0.007999013,0.0003924792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04289952,0.0003852441,0.9471992,0.0002483923,0.006361776,0.002359011,0.00002310862,0.0004898751,0.00003381912],"genre_scores_gemma":[0.9925355,0.0004640626,0.003864085,0.0006451308,0.001276928,0.0007728458,0.00001342848,0.00007282951,0.0003551831],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.949636,"threshold_uncertainty_score":0.9998714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07637608821862601,"score_gpt":0.282548393209856,"score_spread":0.20617230499123,"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."}}