{"id":"W4409032727","doi":"10.1049/icp.2025.0872","title":"Optimizing sampling for Ontario's K-12 wireless network data","year":2025,"lang":"en","type":"article","venue":"IET conference proceedings.","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBI Group (Canada); Humber Polytechnic","funders":"","keywords":"Wireless network; Computer science; Sampling (signal processing); Computer network; Wireless; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.008305538,0.0003464165,0.0004793185,0.0007835384,0.0008151174,0.0007788341,0.0009961363,0.000485556,0.0007375161],"category_scores_gemma":[0.04122784,0.0003355732,0.0003321157,0.001076014,0.000915824,0.0007003329,0.00131937,0.0005631671,0.0001693667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002772733,"about_ca_system_score_gemma":0.006033325,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1472227,"about_ca_topic_score_gemma":0.2004556,"domain_scores_codex":[0.9946035,0.003572097,0.0002293331,0.0005651505,0.0007424995,0.0002875733],"domain_scores_gemma":[0.9837376,0.01151,0.001160405,0.001406291,0.001912354,0.000273416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0007768251,0.0003037101,0.1314218,0.0003291668,0.0001720617,0.000410887,0.002128944,0.602639,0.007149003,0.03431895,0.003202731,0.217147],"study_design_scores_gemma":[0.00007791389,0.0001847187,0.02489506,0.00003433693,0.00002381116,0.00006255352,0.0007437052,0.9549616,0.002604439,0.01383445,0.00255163,0.00002575367],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2118306,0.0001175285,0.7837979,0.00045202,0.00001537138,0.001047422,0.0005404598,0.0002890494,0.001909757],"genre_scores_gemma":[0.7004789,0.0001079672,0.2971621,0.0000874909,0.00001377387,0.0006669192,0.0006101768,0.00002544717,0.0008472149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8527772,"threshold_uncertainty_score":0.2927316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09834510392080827,"score_gpt":0.3206549850088284,"score_spread":0.2223098810880202,"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."}}