{"id":"W2784331297","doi":"10.1109/tsp.2018.2795540","title":"Sparse Activity Detection for Massive Connectivity","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":389,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fading; Computer science; Channel (broadcasting); Base station; False alarm; Algorithm; Compressed sensing; Wireless; Signature (topology); Mathematics; Telecommunications; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009738021,0.0001993751,0.0001790621,0.0001500444,0.0003949845,0.00007436103,0.00008718623,0.0001277392,0.00003012336],"category_scores_gemma":[0.000003111801,0.0002093255,0.00009576031,0.0002228717,0.0000930396,0.0003104414,5.156921e-7,0.0002283923,0.0000174446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001026229,"about_ca_system_score_gemma":0.00002701621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001121267,"about_ca_topic_score_gemma":0.00005213065,"domain_scores_codex":[0.9991735,0.00002552047,0.0001382421,0.0002571933,0.0001348685,0.0002706879],"domain_scores_gemma":[0.99949,0.00009004422,0.00004562172,0.0001497454,0.0001597115,0.00006489297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000131086,0.00006429444,0.000001336609,0.00003851905,0.00003655528,0.000001735518,0.0001493195,0.01794773,0.4265722,0.000003609995,0.00008023896,0.5549734],"study_design_scores_gemma":[0.0001939917,0.000172562,0.00002539511,0.00006570028,0.00003067443,0.000006863753,0.00002439807,0.2957004,0.703023,0.0002703589,0.0003090745,0.0001776036],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1501434,0.0000210195,0.8476683,0.00002248378,0.0003098257,0.0002425166,0.00001033961,0.0009687841,0.0006133298],"genre_scores_gemma":[0.9972408,0.000004002972,0.002322079,0.00004432102,0.0002152703,0.00008198505,4.722169e-7,0.00005166111,0.00003944758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8470974,"threshold_uncertainty_score":0.8536048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02796180552894003,"score_gpt":0.2590285853425017,"score_spread":0.2310667798135616,"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."}}