{"id":"W4310153922","doi":"10.3390/s22239229","title":"IRS-Enabled Ultra-Low-Power Wireless Sensor Networks: Scheduling and Transmission Schemes","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Wireless sensor network; Scheduling (production processes); Computer science; Base station; Wireless; Computer network; Transmitter power output; Real-time computing; Sensor node; Transmission (telecommunications); Key distribution in wireless sensor networks; Wireless network; Telecommunications; Engineering; Transmitter; 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":[0.001976683,0.0007052446,0.0006528738,0.0005385448,0.0006348856,0.0008275636,0.001436437,0.0004523456,0.0007512613],"category_scores_gemma":[0.003806025,0.0002058442,0.0002976819,0.0009047851,0.0007502607,0.001138829,0.0007067803,0.0005885712,0.0002311606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001035272,"about_ca_system_score_gemma":0.001032333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001417641,"about_ca_topic_score_gemma":0.001688639,"domain_scores_codex":[0.9988911,0.0004461166,0.00006021322,0.0001721338,0.0002614927,0.000168969],"domain_scores_gemma":[0.9974322,0.001012291,0.0006060977,0.0003960133,0.0004209004,0.000132574],"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":[0.001162239,0.0004128942,0.002343224,0.0005562418,0.0001128256,0.0002231518,0.000355041,0.712826,0.05753624,0.03806616,0.002236244,0.1841698],"study_design_scores_gemma":[0.00003208746,0.0004030106,0.0006660592,0.00002885296,0.00003721877,0.0002372268,0.0001068679,0.9737748,0.01407501,0.007957513,0.002651635,0.00002960719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1138653,0.002209776,0.8777582,0.000250579,0.0001751083,0.0001402394,0.00007518968,0.0005670619,0.004958444],"genre_scores_gemma":[0.9284718,0.0007565563,0.06941091,0.00007642003,0.00008037106,0.00006928723,0.00003689452,0.00003146848,0.001066268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001976683,"threshold_uncertainty_score":0.01045382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006821519246657221,"score_gpt":0.2080151598677681,"score_spread":0.2011936406211109,"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."}}