{"id":"W4388739236","doi":"10.1109/comst.2023.3333342","title":"Semantics-Empowered Communications: A Tutorial-Cum-Survey","year":2023,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Semantics (computer science); Programming language","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","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.02094881,0.0004574877,0.0007343076,0.000573304,0.001807989,0.0008603414,0.01437066,0.0002994503,0.00000628227],"category_scores_gemma":[0.002885519,0.0004973359,0.0002434831,0.004159525,0.0005460971,0.0009413244,0.004950461,0.0006861686,0.001934509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002171742,"about_ca_system_score_gemma":0.000563352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001729863,"about_ca_topic_score_gemma":0.0004219696,"domain_scores_codex":[0.9866667,0.009532438,0.001400993,0.0007544903,0.0007304187,0.0009149262],"domain_scores_gemma":[0.9745485,0.008642022,0.0005824906,0.01490107,0.001072579,0.0002533433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000510223,0.002015112,0.02728576,0.0001585219,0.0009429667,0.00003441681,0.01600247,0.0003021881,0.01455739,0.07361235,0.7770305,0.08800729],"study_design_scores_gemma":[0.001793715,0.000154299,0.07106916,0.0002281975,0.00006736474,0.00002570151,0.0001095133,0.05538159,0.001610431,0.01269592,0.8549486,0.001915466],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.07362895,0.006073794,0.4152487,0.02969564,0.4239134,0.005528785,0.0002368153,0.0157505,0.02992346],"genre_scores_gemma":[0.9368297,0.003043253,0.04869764,0.0003870702,0.007213789,0.000384481,0.001089424,0.0001609558,0.00219371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8632007,"threshold_uncertainty_score":0.9997478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1480421949447179,"score_gpt":0.3625868663705714,"score_spread":0.2145446714258535,"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."}}