{"id":"W4225998828","doi":"10.48550/arxiv.2112.04550","title":"NOMA Empowered Integrated Sensing and Communication","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Noma; Computer science; Base station; Beamforming; Throughput; Power (physics); SIGNAL (programming language); Single antenna interference cancellation; Dual (grammatical number); Distributed computing; Computer network; Wireless; Telecommunications; Telecommunications link; Channel (broadcasting)","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":[0.0009448594,0.001312399,0.000929369,0.0004667373,0.0005675989,0.001213007,0.001326689,0.0009248547,0.001559187],"category_scores_gemma":[0.001990684,0.0003791839,0.0004977427,0.0008153368,0.0009001555,0.001314938,0.001904268,0.001268689,0.0005362196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004822925,"about_ca_system_score_gemma":0.001225861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001156784,"about_ca_topic_score_gemma":0.002370999,"domain_scores_codex":[0.9987839,0.0004708366,0.00003132268,0.0002171502,0.0003267411,0.0001701268],"domain_scores_gemma":[0.999069,0.000394958,0.0001365647,0.0001339854,0.000202354,0.00006304041],"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":[0.0002804849,0.0001461889,0.0009952891,0.0003783799,0.0001418907,0.0006480488,0.0001797807,0.6657506,0.02904245,0.1610573,0.004782935,0.1365966],"study_design_scores_gemma":[0.000008978685,0.00009321964,0.0001291996,0.00001180628,0.00001586415,0.000150751,0.00002607461,0.9834867,0.002219788,0.01148175,0.002360739,0.00001520304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007369089,0.0003912485,0.9877051,0.0001716787,0.00009005669,0.00002909727,0.00004519404,0.0001039181,0.00409462],"genre_scores_gemma":[0.7419923,0.0008747734,0.2465651,0.0003256368,0.0002266353,0.000179927,0.0001292966,0.00003919503,0.009667154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001559187,"threshold_uncertainty_score":0.005216062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03618196746500894,"score_gpt":0.1604290382256456,"score_spread":0.1242470707606366,"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."}}