{"id":"W4379740689","doi":"10.1109/iotm.001.2200256","title":"RIS-IoE for Data-Driven Networks: New Mentalities, Trends and Preliminary Solutions","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Magazine","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Wireless; Implementation; Physical layer; Constructive; Wireless network; Internet of Things; Computer architecture; Process (computing); Telecommunications; Embedded system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001288356,0.0001464019,0.000210967,0.0002115624,0.00003666254,0.00002345173,0.0007970123,0.00008969483,0.000021909],"category_scores_gemma":[0.00005932732,0.000155714,0.00003832724,0.0002578447,0.0001035723,0.0004175381,0.0005256897,0.000165081,0.00001428166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004264534,"about_ca_system_score_gemma":0.000007491315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003984355,"about_ca_topic_score_gemma":0.00002260103,"domain_scores_codex":[0.9991661,0.00001009791,0.000293832,0.0001942169,0.00009144957,0.0002443478],"domain_scores_gemma":[0.9989387,0.0001707835,0.00008333071,0.0007380592,0.00002542332,0.00004370974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005254104,0.00003252236,0.0001103579,0.0001690389,0.0001995821,0.000003043985,0.0009252503,0.1240503,0.003194843,0.00223943,0.6024948,0.2665283],"study_design_scores_gemma":[0.0003648392,0.0001021888,0.0003039975,0.0001109707,0.00002464625,0.000006158942,0.00012843,0.9651603,0.001660734,0.00127748,0.03069233,0.0001679043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02176133,0.004776283,0.9627391,0.002245018,0.001054963,0.0004565884,0.0002180113,0.004265768,0.002482952],"genre_scores_gemma":[0.9673978,0.001408231,0.02537178,0.00003936891,0.00006129462,0.00005146324,0.0004296152,0.00005580579,0.005184624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9456365,"threshold_uncertainty_score":0.6349832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.052943918396016,"score_gpt":0.2849319600617099,"score_spread":0.2319880416656939,"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."}}