{"id":"W4382119288","doi":"10.1109/mcom.003.2200306","title":"Model Drift in Dynamic Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Communications Magazine","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Concept drift; Rendering (computer graphics); Component (thermodynamics); Process (computing); Distributed computing; Real-time computing; Artificial intelligence; Machine learning; Data stream mining","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":[0.002775771,0.0009143284,0.0009638352,0.0008410893,0.0009910129,0.002399043,0.002006646,0.001678354,0.001472057],"category_scores_gemma":[0.01191932,0.0005321004,0.0007439952,0.0008247909,0.001485115,0.004122844,0.002444976,0.002169554,0.0003788767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00230865,"about_ca_system_score_gemma":0.001684658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01320542,"about_ca_topic_score_gemma":0.005261271,"domain_scores_codex":[0.9985618,0.0004074611,0.00008549316,0.0003591585,0.0003768721,0.0002092958],"domain_scores_gemma":[0.9962624,0.001966042,0.0004432764,0.000478737,0.0007006529,0.0001489567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006619861,0.00001845442,0.002013792,0.00004131362,0.00002953889,0.0001675308,0.0001945242,0.928862,0.0008644332,0.05282008,0.001405826,0.01351627],"study_design_scores_gemma":[0.000005609796,0.00001525941,0.0001821444,0.00001006771,0.000007590568,0.00004972951,0.00003738561,0.9729865,0.0003056382,0.02477955,0.001610952,0.000009650015],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04952941,0.0008100832,0.9403725,0.001752278,0.0001951132,0.00006827295,0.0003207454,0.0006397002,0.006311944],"genre_scores_gemma":[0.9309872,0.001190261,0.06201652,0.0004563845,0.0001448949,0.0001283767,0.0004502736,0.0001595468,0.004466488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01320542,"threshold_uncertainty_score":0.02625716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04543321155449939,"score_gpt":0.3022399058567389,"score_spread":0.2568066943022396,"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."}}