{"id":"W2575241212","doi":"10.48550/arxiv.1611.03183","title":"Massive Machine Type Communication with Data Aggregation and Resource Scheduling","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Scheduling (production processes); Computer science; Data aggregator; Stochastic geometry; Base station; Computer network; Channel (broadcasting); Schedule; Distributed computing; Mathematical optimization; Wireless sensor network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001188046,0.0005925552,0.0008219113,0.0004439024,0.0006500181,0.001248684,0.001114973,0.0008034613,0.00137779],"category_scores_gemma":[0.003239724,0.00032131,0.0005343209,0.001091158,0.0009849233,0.001440232,0.001022235,0.0006245013,0.0002529156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001044179,"about_ca_system_score_gemma":0.000759228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002403616,"about_ca_topic_score_gemma":0.002274845,"domain_scores_codex":[0.9989969,0.0003667536,0.00004042005,0.0001772767,0.0002157639,0.0002028872],"domain_scores_gemma":[0.9976993,0.00119282,0.0004227143,0.0003403129,0.0002279767,0.0001168307],"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":[0.0001262276,0.00005243681,0.001069583,0.00007479952,0.00003014359,0.0003907049,0.00005189935,0.9259812,0.004686954,0.0514186,0.001647152,0.01447032],"study_design_scores_gemma":[0.000005780374,0.00002946165,0.0001710363,0.00000215771,0.000003604538,0.00005889059,0.00001066787,0.9930328,0.0005411951,0.005668515,0.0004699427,0.000005851469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08564361,0.0004153298,0.9069429,0.0003587138,0.0001441409,0.00008996737,0.0001464605,0.0003108728,0.005947876],"genre_scores_gemma":[0.9589947,0.0001639429,0.03929389,0.00007587277,0.00007375586,0.00005418386,0.00006292731,0.00001868908,0.001262026],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002403616,"threshold_uncertainty_score":0.007576108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06976119469944997,"score_gpt":0.1961790250519443,"score_spread":0.1264178303524943,"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."}}