{"id":"W3093767774","doi":"10.1109/iscc50000.2020.9219701","title":"Ensemble Learning Based Sleeping Cell Detection in Cloud Radio Access Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Unavailability; Computer science; Cloud computing; Ensemble learning; TRACE (psycholinguistics); Machine learning; Key (lock); Cellular network; Field (mathematics); Benchmark (surveying); Phone; Artificial intelligence; Distributed computing; Data mining; Computer network; Computer security; Engineering; Reliability engineering","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":[0.001001719,0.0009251314,0.001160349,0.000783944,0.0004291851,0.0006399869,0.001299472,0.0005742109,0.0003668088],"category_scores_gemma":[0.002185541,0.0002123987,0.0004449216,0.0006752447,0.0002944039,0.0009636818,0.0007832133,0.0007423748,0.0001778304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006174442,"about_ca_system_score_gemma":0.0004950187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008724179,"about_ca_topic_score_gemma":0.008486061,"domain_scores_codex":[0.9993332,0.0001420719,0.00003240067,0.0001994691,0.000140172,0.0001525755],"domain_scores_gemma":[0.998996,0.0003719743,0.0001383317,0.0001110113,0.0002964263,0.00008625296],"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":[0.0003151841,0.0002734368,0.02757716,0.00006560441,0.0001560525,0.0002958821,0.0001475792,0.6413373,0.006916471,0.001836575,0.003763071,0.3173157],"study_design_scores_gemma":[0.000002049517,0.00002244103,0.001171044,0.000002535351,0.000009474062,0.00002880719,0.00001501556,0.9974712,0.0007258542,0.0004059622,0.0001411753,0.000004433371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2665578,0.001189197,0.7275445,0.0003897261,0.000147966,0.00007492066,0.0002935785,0.001403636,0.002398686],"genre_scores_gemma":[0.9615245,0.0002191047,0.03686907,0.000111008,0.00005580437,0.00002697807,0.0002927628,0.0000232564,0.0008774986],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008724179,"threshold_uncertainty_score":0.0173468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02844706156408276,"score_gpt":0.2872471444814258,"score_spread":0.258800082917343,"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."}}