{"id":"W4287169011","doi":"10.48550/arxiv.2105.12871","title":"Random Access Based on Maximum Average Distance Code for Massive MTC in\\n Cellular IoT Networks","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo; Government of Canada","keywords":"Random access; Code (set theory); Computer science; Decoding methods; Ambiguity; Scheme (mathematics); Channel (broadcasting); Algorithm; Inference; Internet of Things; Theoretical computer science; Computer network; Mathematics; Artificial intelligence; Embedded system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002522384,0.0004891539,0.0006248446,0.0001912255,0.0001052848,0.0001926857,0.0008192598,0.0005703318,0.0001147409],"category_scores_gemma":[0.00001894779,0.0005995675,0.0003431179,0.0004459132,0.0000531281,0.0001143828,0.0002770081,0.0009704935,0.000005740239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003750772,"about_ca_system_score_gemma":0.00009238555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002229668,"about_ca_topic_score_gemma":0.0001341278,"domain_scores_codex":[0.9981037,0.0001160435,0.0002960583,0.0008303021,0.0000824932,0.0005713397],"domain_scores_gemma":[0.9984989,0.0003000146,0.0001402861,0.0008153406,0.00009896214,0.0001464925],"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":[0.0004641271,0.00005555402,0.001427577,0.0003723005,0.00008267295,0.0003794775,0.00002757983,0.9956872,0.000008536409,0.0007400158,0.0005209938,0.0002338942],"study_design_scores_gemma":[0.003316428,0.00003389316,0.000165785,0.000593335,0.00006123792,3.10229e-7,0.00001607676,0.9900161,0.0002184069,0.002219297,0.002730336,0.0006288194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02605043,0.0001484592,0.9649503,0.00002466079,0.0007921768,0.005928053,0.00008939005,0.0001990789,0.001817512],"genre_scores_gemma":[0.9982654,0.0001348718,0.0002992738,0.000117113,0.0002801441,0.0002358843,0.000220258,0.00009372363,0.0003533068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.972215,"threshold_uncertainty_score":0.9996456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04427142482977586,"score_gpt":0.1963998540023073,"score_spread":0.1521284291725315,"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."}}