{"id":"W4390738767","doi":"10.1109/mnet.2024.3352917","title":"Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation","year":2024,"lang":"en","type":"article","venue":"IEEE Network","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Content (measure theory); Encoder; Artificial intelligence; Semantics (computer science); Semantic network; Semantic computing; Information retrieval; Semantic Web","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":[0.002320679,0.0004996069,0.0003584565,0.001105598,0.0006359384,0.002618439,0.001021265,0.00129816,0.003052836],"category_scores_gemma":[0.005835486,0.0002621418,0.0004545492,0.00129088,0.001812343,0.004693378,0.002225939,0.001594985,0.0008380624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001709744,"about_ca_system_score_gemma":0.001654423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001359728,"about_ca_topic_score_gemma":0.001593315,"domain_scores_codex":[0.9985183,0.0006506221,0.00007893717,0.0002010339,0.0004621572,0.00008893217],"domain_scores_gemma":[0.9980652,0.0009625627,0.0001790329,0.0003243896,0.0003633438,0.0001054171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008297876,0.0001039299,0.0007693447,0.0002574736,0.0000315882,0.0001751465,0.0003836905,0.04711939,0.01183732,0.7750569,0.00443106,0.1597511],"study_design_scores_gemma":[0.00002004568,0.000101355,0.0005084754,0.0001095475,0.00004004515,0.0002424583,0.0002012692,0.5370697,0.01554734,0.4086612,0.03746399,0.00003451679],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008373612,0.0002841689,0.9798362,0.0007551064,0.00005923405,0.0001279867,0.00006010044,0.0003681673,0.01013535],"genre_scores_gemma":[0.3519933,0.0008672944,0.6378663,0.0003254252,0.0001341748,0.0003258565,0.0002653767,0.0002207401,0.008001478],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003052836,"threshold_uncertainty_score":0.0124051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1497919075868177,"score_gpt":0.3360269212500516,"score_spread":0.1862350136632338,"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."}}