{"id":"W4295973897","doi":"10.4108/eetinis.v9i32.1909","title":"Intelligent Bi-directional Relaying Communication for Edge Intelligence based Industrial IoT Networks","year":2022,"lang":"en","type":"article","venue":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Rayleigh fading; Enhanced Data Rates for GSM Evolution; Computer science; Channel (broadcasting); Group (periodic table); Range (aeronautics); Communications system; Channel state information; Order (exchange); Fading; Signal-to-noise ratio (imaging); Telecommunications; Wireless; Engineering; Business; Physics","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","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001589086,0.0005257848,0.0006148894,0.0005734682,0.001418941,0.0002463948,0.0008204138,0.0005582077,0.0002918277],"category_scores_gemma":[0.00004899382,0.0006049768,0.0002979109,0.0010011,0.0001592983,0.0001690709,0.00004753728,0.002558438,0.000005801543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000702375,"about_ca_system_score_gemma":0.00009330799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002037263,"about_ca_topic_score_gemma":0.00004187224,"domain_scores_codex":[0.996304,0.0006896249,0.001353328,0.0005460596,0.0004883095,0.0006186638],"domain_scores_gemma":[0.9968595,0.001449073,0.0002976165,0.0009922734,0.0001551069,0.0002464674],"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":[0.0002537313,0.0001808108,0.00004544089,0.00002007769,0.0001581961,0.000001044002,0.0001780309,0.9421387,0.00001698064,0.00117597,0.004037702,0.05179334],"study_design_scores_gemma":[0.0006388334,0.0003434772,0.000002861453,0.0002137641,0.00007577502,0.00001890606,0.0008860039,0.8596259,0.001211733,0.00005339789,0.1364039,0.00052552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001626803,0.002753502,0.9870001,0.0003002076,0.004167005,0.002634758,0.0001244137,0.0009899028,0.0004032852],"genre_scores_gemma":[0.9936759,0.001364942,0.0007394642,0.0001042722,0.0006937078,0.002829127,0.0002533065,0.0001361568,0.0002031594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.992049,"threshold_uncertainty_score":0.9998811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06043244985038614,"score_gpt":0.2573885266375754,"score_spread":0.1969560767871892,"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."}}