{"id":"W3090535981","doi":"10.1145/3416010.3423217","title":"NeuRA","year":2020,"lang":"en","type":"article","venue":"","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Throughput; Sampling (signal processing); Frame (networking); Artificial neural network; Adaptation (eye); Variety (cybernetics); Sampling frame; Algorithm; Real-time computing; Machine learning; Artificial intelligence; Computer network; Wireless; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004730679,0.0007052359,0.000371805,0.0003463684,0.0003534101,0.001059368,0.00106885,0.0009590458,0.01152515],"category_scores_gemma":[0.001421482,0.0002039325,0.0003561762,0.0004091314,0.0003238774,0.0009288737,0.0007092445,0.0008876865,0.00574274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005387908,"about_ca_system_score_gemma":0.0007319326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002445197,"about_ca_topic_score_gemma":0.002423804,"domain_scores_codex":[0.999842,0.00002624076,0.00001131837,0.00005044094,0.00004977486,0.00002005233],"domain_scores_gemma":[0.9997205,0.00006650745,0.00003626671,0.00004272398,0.0001153533,0.00001859808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002121247,0.0001029376,0.001688977,0.0003252181,0.0001167892,0.0001847988,0.00006384876,0.1531326,0.01025185,0.06799348,0.04447863,0.7214488],"study_design_scores_gemma":[0.00004852296,0.00009353036,0.0007676686,0.00005630684,0.0000389235,0.000288466,0.00003280383,0.9107667,0.006769769,0.02212267,0.05897621,0.0000384173],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01635706,0.004286089,0.9049836,0.002265922,0.001244796,0.0001741653,0.0008680457,0.003412269,0.06640807],"genre_scores_gemma":[0.5213274,0.009494039,0.3197757,0.002142958,0.0004825029,0.0008841884,0.002528821,0.0003577289,0.1430067],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9884748,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03294977198091718,"score_gpt":0.229913104052632,"score_spread":0.1969633320717148,"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."}}