{"id":"W4388651705","doi":"10.36227/techrxiv.24527455.v1","title":"Semantic Communication: A Survey on Research Landscape, Challenges, and Future Directions","year":2023,"lang":"en","type":"preprint","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"National Institute of Standards and Technology; U.S. Department of Commerce","keywords":"Computer science; Data science; Paradigm shift; Status quo; Context (archaeology); Semantic integration; World Wide Web; Semantic computing; Semantic Web; Political science","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":[],"consensus_categories":[],"category_scores_codex":[0.003738699,0.0001981588,0.0002671187,0.0004172779,0.0005193665,0.0004116257,0.001566251,0.0002654553,0.000003035923],"category_scores_gemma":[0.0001084666,0.0001776764,0.00005433342,0.0004508744,0.00006138989,0.00008219299,0.004995139,0.001404823,0.0001604348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000409739,"about_ca_system_score_gemma":0.0001209187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001114468,"about_ca_topic_score_gemma":0.001237995,"domain_scores_codex":[0.9972435,0.0009618539,0.0002378223,0.0007280427,0.0004449392,0.0003837998],"domain_scores_gemma":[0.9963973,0.001232933,0.0000580958,0.001911908,0.0002920582,0.0001077707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002264983,0.0003271584,0.002596182,0.0005042283,0.00022572,0.00004306645,0.01472334,0.00008335174,0.000001864898,0.04471056,0.389593,0.5471689],"study_design_scores_gemma":[0.0003100781,0.0001233753,0.6904335,0.0005334783,0.000008862124,0.00001673619,0.0002341021,0.05709657,0.000008956383,0.02189495,0.2286437,0.0006956447],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"review","genre_scores_codex":[0.06105895,0.1853135,0.0184478,0.264066,0.1362096,0.00426921,0.00002121706,0.009792373,0.3208213],"genre_scores_gemma":[0.3906466,0.4912201,0.04462179,0.0008889175,0.03859568,0.0005145451,0.0007334983,0.0003703421,0.03240842],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.6878373,"threshold_uncertainty_score":0.7245434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2335033143011106,"score_gpt":0.3763190243992491,"score_spread":0.1428157100981385,"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."}}