{"id":"W4389571266","doi":"10.3390/app132413114","title":"Resource-Aware Federated Hybrid Profiling for Edge Node Selection in Federated Patient Similarity Network","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Humber Polytechnic","funders":"","keywords":"Computer science; Edge computing; Cloud computing; Quality of service; Distributed computing; Enhanced Data Rates for GSM Evolution; Profiling (computer programming); Edge device; Node (physics); Computer network; Data mining; Artificial intelligence; Operating system","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":[0.001689554,0.0006606445,0.0008911059,0.0008450627,0.0006856651,0.001125272,0.001133851,0.0006682905,0.000593088],"category_scores_gemma":[0.004636064,0.0002377697,0.0003589265,0.0007885325,0.0003763581,0.001899282,0.001289848,0.0005250796,0.0001647376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008839424,"about_ca_system_score_gemma":0.0009802184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002876266,"about_ca_topic_score_gemma":0.004543536,"domain_scores_codex":[0.9987713,0.0004564775,0.00007005425,0.0003087748,0.0002308115,0.0001625547],"domain_scores_gemma":[0.9984951,0.0005613468,0.0002097912,0.0002964165,0.0002957089,0.0001416389],"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":[0.0008085487,0.0004739213,0.02119446,0.0001248098,0.0001558986,0.0003945418,0.000418337,0.6975643,0.01614062,0.01082479,0.003294312,0.2486054],"study_design_scores_gemma":[0.000005906104,0.00005145515,0.0009864345,0.000005280995,0.00001166883,0.0000765261,0.00005016847,0.9925737,0.00259893,0.003239942,0.0003913395,0.000008504189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1516516,0.0003181205,0.8440875,0.0004111172,0.00007275683,0.000130735,0.0001911888,0.00145072,0.001686316],"genre_scores_gemma":[0.933133,0.0000685126,0.06587928,0.0001187753,0.00001666311,0.00004316373,0.0001687151,0.00002403712,0.0005478576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002876266,"threshold_uncertainty_score":0.008935332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03271451044599577,"score_gpt":0.2618716434740733,"score_spread":0.2291571330280776,"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."}}