{"id":"W2767822391","doi":"10.1109/tgcn.2017.2772079","title":"Performance Characterization of Spatially Random Energy Harvesting Underlay D2D Networks With Transmit Power Control","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Green Communications and Networking","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Energy harvesting; Transmitter; Rayleigh fading; Path loss; Underlay; Computer science; Power control; Transmitter power output; Fading; Telecommunications link; Energy (signal processing); Channel (broadcasting); Computer network; Telecommunications; Wireless; Power (physics); Mathematics; Signal-to-noise ratio (imaging); Physics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002563687,0.0002724437,0.0003630077,0.0001054041,0.00111926,0.0001406639,0.0006362181,0.0001557727,0.000008408576],"category_scores_gemma":[5.70978e-7,0.0002674028,0.0000711922,0.0001594758,0.000307107,0.0004037097,0.000006781078,0.0004159726,5.421264e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003490359,"about_ca_system_score_gemma":0.00002242008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002581628,"about_ca_topic_score_gemma":0.001044069,"domain_scores_codex":[0.9987651,0.0001006463,0.0004356307,0.0002201993,0.000161628,0.0003167845],"domain_scores_gemma":[0.9978995,0.0003226337,0.0002462486,0.001342763,0.00009375104,0.00009506498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000101917,0.0000399502,0.001008137,0.00003151646,0.000170669,0.000001181669,0.0001307115,0.4199811,0.001111473,0.00003213494,0.000001625035,0.5773896],"study_design_scores_gemma":[0.001412932,0.0001170311,0.001757919,0.0005848563,0.0001004959,0.00001179594,0.000005250886,0.994161,0.0004579104,0.000007326406,0.001093099,0.0002904499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0495305,0.0003461113,0.9476652,0.0001859132,0.0003051408,0.0001701637,0.00001186345,0.0001916128,0.00159347],"genre_scores_gemma":[0.9937403,0.00431708,0.001476966,0.00006167552,0.0001432264,0.00005857403,0.00001619076,0.00007617799,0.0001097649],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9461883,"threshold_uncertainty_score":0.9999778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01326151873744668,"score_gpt":0.2002321395471508,"score_spread":0.1869706208097041,"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."}}