{"id":"W2753791741","doi":"10.1109/ita.2017.8023482","title":"On the fundamental limits of massive connectivity","year":2017,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Channel (broadcasting); Base station; Coherence (philosophical gambling strategy); Coherence time; Identification (biology); Transmission (telecommunications); Telecommunications link; Upper and lower bounds; Compressed sensing; Interval (graph theory); Phase (matter); Real-time computing; Computer network; Electronic engineering; Telecommunications; Algorithm; Engineering; Physics","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":[0.00003504636,0.00005069195,0.00006326547,0.00001156224,0.00008349285,0.00002600339,0.0001440928,0.00002185457,0.00006336532],"category_scores_gemma":[0.00003711441,0.00003240068,0.00002774987,0.000008101787,0.00004594869,0.00003730258,0.00002390036,0.00005624063,0.00001246981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009206614,"about_ca_system_score_gemma":0.000002111629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002734139,"about_ca_topic_score_gemma":0.00001216631,"domain_scores_codex":[0.9997817,0.000006934069,0.00004452374,0.00004839745,0.00005468681,0.00006376422],"domain_scores_gemma":[0.9995359,0.00007705618,0.00002407928,0.000337799,0.00001381307,0.00001141575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005573378,0.0001442308,0.005094543,0.00002657436,0.0002886551,0.00002979955,0.0004357172,0.002957781,0.400802,0.4503896,0.1089941,0.03078131],"study_design_scores_gemma":[0.00008525843,0.00004655188,0.01266277,0.0000383832,0.000005082349,0.000001200505,0.0000390564,0.01000802,0.9692638,0.007161858,0.0006082356,0.00007977412],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8653516,0.00001002889,0.001172817,0.0002076416,0.0001046062,0.00006336118,0.000001861202,0.0001420835,0.1329461],"genre_scores_gemma":[0.9996667,0.000006615202,0.0001512664,0.00005017518,0.00001793755,0.000002697366,1.889256e-7,0.000006747726,0.00009770568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5684618,"threshold_uncertainty_score":0.1321262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0406523164402643,"score_gpt":0.262928693624101,"score_spread":0.2222763771838367,"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."}}