{"id":"W4317425337","doi":"10.18280/mmep.090634","title":"Improving the Reliability of RPL Using Hybrid Deep Learning and Objective Function-Based DODAG Structure for AMI","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Computer science; Routing protocol; Routing (electronic design automation); Reliability engineering; Smart grid; Computer network; Protocol (science); Power (physics); Engineering; Electrical engineering; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003518747,0.0001540377,0.0002108595,0.00005822799,0.0002730295,0.00003228816,0.0000793537,0.00004100816,0.000007184637],"category_scores_gemma":[0.0000655326,0.0001236314,0.00004896565,0.0001098149,0.00005195242,0.00005671148,0.00004836145,0.00036031,8.404668e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003928361,"about_ca_system_score_gemma":0.00001013597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001213841,"about_ca_topic_score_gemma":4.775599e-7,"domain_scores_codex":[0.9991705,0.00001624878,0.0002458935,0.0001936926,0.0001489264,0.0002247163],"domain_scores_gemma":[0.9993897,0.0003664803,0.00004027512,0.0001206421,0.00003274951,0.00005016317],"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":[0.000009564417,0.0000102947,0.00003162817,0.001157125,0.00001611556,2.499404e-7,0.000605422,0.9929646,0.00426328,0.0006431671,9.393171e-7,0.0002975771],"study_design_scores_gemma":[0.0001850691,0.00006744509,0.000007791978,0.00005744192,0.00004024537,0.000008994668,0.0001415,0.9914702,0.001105153,0.006707339,0.00006065776,0.0001481983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4075654,0.0004140608,0.5916298,0.000008318195,0.00008607304,0.0001999726,0.000008599461,0.00008030567,0.000007472383],"genre_scores_gemma":[0.9860746,0.00001166008,0.01378458,0.000005329563,0.00003412503,0.00004956973,0.000003758229,0.00003152231,0.000004835098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5785092,"threshold_uncertainty_score":0.5041543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008302399853821838,"score_gpt":0.1845911925642,"score_spread":0.1762887927103781,"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."}}