{"id":"W4415593467","doi":"10.1109/tmc.2025.3625263","title":"A QoE-Driven Personalized Incentive Mechanism Design for AIGC Services in Resource-Constrained Edge Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Natural Science Foundation of Guangdong Province; Queen's University; National Natural Science Foundation of China; Queen's University Belfast","keywords":"Server; Mobile edge computing; Enhanced Data Rates for GSM Evolution; Service (business); Service provider; Key (lock); Incentive; Edge device; Resource allocation; Mobile telephony","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007836037,0.0003243961,0.0004503004,0.0003664754,0.0005178766,0.0002488284,0.0009820395,0.0001845246,0.000006047191],"category_scores_gemma":[0.000005184935,0.000344642,0.0002297374,0.001061874,0.00007442999,0.0001752339,0.00001786444,0.0003860885,0.000007883638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001746922,"about_ca_system_score_gemma":0.0001423017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003182807,"about_ca_topic_score_gemma":0.00001694365,"domain_scores_codex":[0.9974484,0.0003689157,0.0005799055,0.0007700472,0.0002184902,0.0006142345],"domain_scores_gemma":[0.9981236,0.001016819,0.0001828023,0.0004670772,0.0001114655,0.00009822854],"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":[0.0000687065,0.0001826228,0.00001297469,0.00009259112,0.00007847971,0.000008841979,0.001659913,0.9647123,0.0001602698,0.005006337,0.0001040357,0.02791293],"study_design_scores_gemma":[0.001620523,0.0001812395,0.0000241055,0.0006048958,0.0000231361,0.000007726621,0.0003863083,0.9940171,0.0007962506,0.0007981885,0.001244294,0.0002962644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006868546,0.0001191884,0.9894747,0.000145027,0.001257258,0.001448374,0.00002093514,0.0004092172,0.0002567347],"genre_scores_gemma":[0.9657815,0.00000578255,0.03339936,0.000346292,0.00007797901,0.0001685196,0.000007386697,0.00001920416,0.0001940498],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9589129,"threshold_uncertainty_score":0.9999006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01547451007323534,"score_gpt":0.2570238871812326,"score_spread":0.2415493771079973,"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."}}