{"id":"W4291722496","doi":"10.1145/3524844.3528053","title":"Learning self-adaptations for IoT networks","year":2022,"lang":"en","type":"article","venue":"","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Adaptation (eye); Distributed computing; Flexibility (engineering); Context (archaeology); Network packet; Interoperability; Software-defined networking; Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000994947,0.0005861955,0.0004695607,0.0003434742,0.000349948,0.0006275669,0.0009638338,0.0009154106,0.0008006487],"category_scores_gemma":[0.003597744,0.0002646241,0.0003996622,0.0002642678,0.0009416798,0.0008368363,0.0009137727,0.001105469,0.0001230203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007883204,"about_ca_system_score_gemma":0.0007570988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002984999,"about_ca_topic_score_gemma":0.002471566,"domain_scores_codex":[0.999638,0.0001126339,0.00001805847,0.00009491957,0.00008896476,0.00004735687],"domain_scores_gemma":[0.9986857,0.0008419605,0.0001542959,0.0001135564,0.0001410321,0.00006348461],"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":[0.00002283583,0.00006058565,0.001225742,0.00002985375,0.0000251338,0.00005466253,0.00006389729,0.9641002,0.001747257,0.004796625,0.000346252,0.027527],"study_design_scores_gemma":[0.000005087506,0.00001764894,0.0001401458,0.000004166538,0.000005431056,0.00001253406,0.00001268111,0.9939548,0.0004365843,0.005047571,0.0003604886,0.00000277288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1172603,0.00038899,0.8770304,0.0006400346,0.00005837145,0.00009378218,0.00003355145,0.0006912522,0.003803371],"genre_scores_gemma":[0.908209,0.0002552864,0.08949666,0.0002175686,0.00003245296,0.0001268141,0.0000583207,0.00007381968,0.001529988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002984999,"threshold_uncertainty_score":0.005935252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01289011320629912,"score_gpt":0.2219046421287478,"score_spread":0.2090145289224487,"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."}}