{"id":"W3095023944","doi":"10.1109/tcomm.2020.3034934","title":"Computation Over Multi-Access Channels: Multi-Hop Implementation and Resource Allocation","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Computation; Wireless; Hop (telecommunications); Superposition principle; Fading; Wireless network; Distributed computing; Computer network; Resource allocation; Channel (broadcasting); Algorithm; Mathematics; Telecommunications","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":[0.0004572878,0.0003080125,0.0003351079,0.0003061663,0.0005007228,0.0007803891,0.000788849,0.0004724953,0.001808818],"category_scores_gemma":[0.001383408,0.0001608773,0.0002845085,0.0005692217,0.0006486319,0.001183899,0.0007822944,0.0007182026,0.0002586971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006016713,"about_ca_system_score_gemma":0.0009605808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002119995,"about_ca_topic_score_gemma":0.002645635,"domain_scores_codex":[0.9996282,0.0001053421,0.00001775234,0.00005255962,0.0001303937,0.00006571838],"domain_scores_gemma":[0.9995956,0.0001639176,0.00002606228,0.0001185375,0.00007234118,0.00002353314],"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":[0.0002187076,0.0001369673,0.001421467,0.0001680674,0.00004910947,0.0001504525,0.0002274115,0.4584917,0.02123007,0.3172232,0.004751869,0.1959311],"study_design_scores_gemma":[0.000009710478,0.0000263515,0.00009212158,0.000006401558,0.000006569436,0.00003159617,0.00002055889,0.9742877,0.003309895,0.01967272,0.002528316,0.00000796968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02201584,0.0004571022,0.9698479,0.00029635,0.00006341314,0.00006046407,0.00002713475,0.0003198287,0.00691199],"genre_scores_gemma":[0.7293374,0.00055759,0.2668967,0.0001299271,0.00005861497,0.000158589,0.0000598162,0.00004670902,0.002754739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002119995,"threshold_uncertainty_score":0.006051123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08917838698410775,"score_gpt":0.345838731161207,"score_spread":0.2566603441770993,"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."}}