{"id":"W3004634489","doi":"10.1109/pacrim47961.2019.8985084","title":"Conditional Training Based GM and GM-OPELM Data Fusion Schemes in Wireless Sensor Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Extreme learning machine; Wireless sensor network; Key (lock); Sensor fusion; Energy consumption; Internet of Things; Reduction (mathematics); Efficient energy use; Artificial intelligence; Wireless; Artificial neural network; Word error rate; Machine learning; Range (aeronautics); Data mining; Computer network; Telecommunications; Engineering; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004278774,0.00009204067,0.0001271901,0.00006740996,0.00006220207,0.0001063354,0.0004759391,0.00005233312,0.0001554739],"category_scores_gemma":[0.00002265399,0.0000787731,0.00001310043,0.0001619382,0.00002204725,0.0003145699,0.0003300079,0.0001582707,0.00003411338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007806204,"about_ca_system_score_gemma":0.00004481737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007687177,"about_ca_topic_score_gemma":0.00002861426,"domain_scores_codex":[0.9990409,0.00007029355,0.0001323568,0.0003947029,0.0001591275,0.0002026392],"domain_scores_gemma":[0.9992381,0.0001692888,0.00003856874,0.0004777913,0.00001678294,0.00005944398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007271779,0.0002893773,0.1549705,0.000124623,0.00004597653,0.00009790961,0.001718338,0.07015677,0.002114012,0.1109519,0.01016775,0.6492901],"study_design_scores_gemma":[0.000543669,0.00002584555,0.01767744,0.00002946193,0.000001165046,0.000007387554,0.00003719656,0.975905,0.00002335207,0.00007721782,0.005555179,0.0001170722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4755852,0.00004061131,0.5189184,0.001740523,0.0001693354,0.0001071151,0.000005671478,0.0001266328,0.003306522],"genre_scores_gemma":[0.9713237,0.000003898876,0.0271294,0.0008315275,0.00005484644,0.000001540463,0.00009461509,0.000005619579,0.0005548269],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9057482,"threshold_uncertainty_score":0.3212274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02854246270825202,"score_gpt":0.2625766262826073,"score_spread":0.2340341635743552,"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."}}