{"id":"W4417508498","doi":"10.1109/tce.2025.3596490","title":"Guest Editorial of the Special section on Lightweight Large Model for Edge Computing in Consumer Devices","year":2025,"lang":"","type":"editorial","venue":"IEEE Transactions on Consumer Electronics","topic":"Big Data and Digital Economy","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Special section; Cloud computing; Edge computing; Enhanced Data Rates for GSM Evolution; Node (physics); Edge device; Section (typography); Applications of artificial intelligence","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","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.001105406,0.001137377,0.001399691,0.000838067,0.001067477,0.0005492892,0.002454295,0.001953585,0.00002226737],"category_scores_gemma":[0.0001284579,0.00107313,0.0009168077,0.001379827,0.0003258331,0.0009489547,0.00004556604,0.003690985,0.00003462346],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001259866,"about_ca_system_score_gemma":0.0066162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008522531,"about_ca_topic_score_gemma":0.004731821,"domain_scores_codex":[0.9929457,0.0003238495,0.001945437,0.00193359,0.001193901,0.001657483],"domain_scores_gemma":[0.9933886,0.002834497,0.001011745,0.001700368,0.0008561146,0.0002087105],"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":[0.001506739,0.002512248,0.00001522938,0.0005974931,0.0006940687,0.000002439665,0.0008367681,0.01610206,0.00006135441,0.002779044,0.9600244,0.01486822],"study_design_scores_gemma":[0.004270892,0.0005478415,0.000003386431,0.0008113841,0.0003590194,0.000002972505,0.00002085823,0.1362433,0.004148016,0.0003337467,0.8523241,0.0009344508],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0003137464,0.000445466,0.2907229,0.0001561466,0.7038098,0.001555835,0.002238996,0.0000676085,0.0006895257],"genre_scores_gemma":[0.07509089,0.0009447834,0.0004483752,0.00022918,0.9214866,0.0002015904,0.000163576,0.0001088212,0.001326162],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.2902745,"threshold_uncertainty_score":0.9993421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01568048981341058,"score_gpt":0.2592435137649283,"score_spread":0.2435630239515177,"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."}}