{"id":"W4403278052","doi":"10.1109/tce.2024.3477349","title":"Consumer-Centric Sustainability: Empowering URLLC in Multi-UAV-Assisted MEC Systems for Industry 5.0","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sustainability; Computer science; Business; Manufacturing engineering; Engineering; Systems engineering","routes":{"ca_aff":true,"ca_fund":true,"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"],"consensus_categories":[],"category_scores_codex":[0.0007120805,0.0003837633,0.0004128137,0.0007132492,0.0003462742,0.0005310067,0.0006435359,0.0004189042,0.000003887373],"category_scores_gemma":[0.00003630596,0.0004042606,0.0002222226,0.001626384,0.00007877038,0.000460093,0.00001029574,0.001474836,0.00003206157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001278213,"about_ca_system_score_gemma":0.001444748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001757298,"about_ca_topic_score_gemma":0.00008176708,"domain_scores_codex":[0.9968156,0.0001593472,0.0006377412,0.0009055772,0.0003082298,0.001173536],"domain_scores_gemma":[0.9981815,0.0006814093,0.00008390938,0.0006476506,0.0002317296,0.0001738128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002243112,0.002101152,0.0008274071,0.002368166,0.001066127,0.0003500097,0.004275153,0.03368202,0.001830651,0.006025122,0.004564819,0.9426851],"study_design_scores_gemma":[0.001640646,0.0002471301,0.0001313806,0.0003095099,0.0001085511,0.0001516756,0.0001938538,0.8986678,0.003217665,0.0003569022,0.09416442,0.0008104624],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03654386,0.008493703,0.9421129,0.0005806756,0.01045279,0.001085379,0.000005708974,0.0006693003,0.00005565363],"genre_scores_gemma":[0.9965589,0.0002502423,0.001902995,0.0000805938,0.0001337602,0.0002577129,0.000003410936,0.00005711247,0.0007552468],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9600151,"threshold_uncertainty_score":0.9998409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02617874571734679,"score_gpt":0.3011587129052802,"score_spread":0.2749799671879334,"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."}}