{"id":"W4366966441","doi":"10.1109/access.2023.3269848","title":"Making Sense of Meaning: A Survey on Metrics for Semantic and Goal-Oriented Communication","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Variety (cybernetics); Wireless; Meaning (existential); Human–computer interaction; Data science; Artificial intelligence; Telecommunications","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":[0.009747066,0.002968267,0.002266485,0.01021317,0.001908762,0.01094857,0.00269375,0.003035934,0.004325476],"category_scores_gemma":[0.03546289,0.0008794957,0.001907081,0.009951107,0.007859681,0.02061717,0.004923319,0.003989175,0.001266276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004908256,"about_ca_system_score_gemma":0.003051564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003579821,"about_ca_topic_score_gemma":0.001934667,"domain_scores_codex":[0.9863296,0.004959195,0.00184884,0.001587898,0.004776164,0.0004982963],"domain_scores_gemma":[0.9732722,0.01817382,0.001953708,0.001912048,0.004053309,0.000634917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007925079,0.00008359941,0.002443368,0.002759373,0.0001565086,0.0001315205,0.001514188,0.006453542,0.0007890558,0.7032799,0.008184029,0.2741256],"study_design_scores_gemma":[0.00001328157,0.0001546652,0.00206052,0.00266299,0.0001631357,0.000566624,0.001898184,0.02849412,0.001547738,0.7979467,0.1643092,0.0001827464],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0102156,0.2257125,0.6863528,0.009908658,0.001506889,0.0004010481,0.001099466,0.0007532568,0.06404971],"genre_scores_gemma":[0.2702263,0.2220223,0.4912991,0.002377994,0.003427789,0.001364845,0.001838193,0.0007248755,0.006718745],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01094857,"threshold_uncertainty_score":0.05154806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08900052266910329,"score_gpt":0.3442245624786222,"score_spread":0.2552240398095189,"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."}}