{"id":"W4388011447","doi":"10.1145/3616391.3622767","title":"GNB-RPL: Gaussian Naïve Bayes for RPL Routing Protocol in Smart Grid Communications","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Atlantic Canada Opportunities Agency","keywords":"Naive Bayes classifier; Computer science; Routing protocol; Network packet; Bayes' theorem; Lossy compression; Computer network; Smart grid; Gaussian; Multiprotocol Label Switching; Node (physics); Wireless; Machine learning; Artificial intelligence; Quality of service; Support vector machine; Engineering; Bayesian probability","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":[],"consensus_categories":[],"category_scores_codex":[0.0006797761,0.0001670315,0.0001944378,0.0002337487,0.0002878513,0.0001899047,0.001986202,0.00009489486,0.00000981736],"category_scores_gemma":[0.00009585696,0.00015314,0.00008340755,0.001377621,0.00006816176,0.0002810185,0.0008740024,0.000206191,0.00007614362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006897251,"about_ca_system_score_gemma":0.00006716961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001377427,"about_ca_topic_score_gemma":0.00109137,"domain_scores_codex":[0.9982358,0.0001277238,0.0004161366,0.0004401363,0.0002267451,0.000553422],"domain_scores_gemma":[0.99764,0.000540726,0.0001129457,0.00154049,0.00007595284,0.00008987375],"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":[0.00002508807,0.0004431905,0.009170355,0.0001006366,0.00003775593,0.00002122753,0.002243364,0.09783026,0.0005111287,0.8149893,0.02159443,0.05303326],"study_design_scores_gemma":[0.0005579694,0.00005118309,0.003229668,0.00008257577,0.000001763092,0.000003052426,0.00009600945,0.959893,0.0006129607,0.0007254023,0.03451926,0.0002270919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004265289,0.00001953296,0.8295847,0.0184575,0.001068677,0.0898711,0.0000132839,0.003524656,0.05319532],"genre_scores_gemma":[0.545023,0.00001058699,0.2775322,0.0009251586,0.0002937175,0.1717984,0.00006881625,0.00007760035,0.004270534],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8620628,"threshold_uncertainty_score":0.6244867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04179400157234727,"score_gpt":0.3249589556084114,"score_spread":0.2831649540360642,"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."}}