{"id":"W4405179107","doi":"10.1109/tmc.2024.3514214","title":"Joint Encoding and Enhancement for Low-Light Video Analytics in Mobile Edge Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Joint (building); Analytics; Encoding (memory); Enhanced Data Rates for GSM Evolution; Mobile telephony; Computer network; Telecommunications; Artificial intelligence; Mobile radio; Data science","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":[0.0008513441,0.0008674564,0.0005989234,0.0003898013,0.000348057,0.0008678284,0.001105666,0.0005648015,0.001690423],"category_scores_gemma":[0.002404968,0.0003154222,0.0003047335,0.0003045137,0.0004782172,0.001890558,0.001295911,0.001135169,0.0005924674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004930459,"about_ca_system_score_gemma":0.0005713345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001362686,"about_ca_topic_score_gemma":0.00187652,"domain_scores_codex":[0.9994138,0.0001164211,0.0000267194,0.0001345766,0.000186565,0.0001218724],"domain_scores_gemma":[0.9990643,0.0003690611,0.0001080372,0.0001429361,0.0002544134,0.00006129316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00187766,0.000631865,0.004497736,0.0002687409,0.0001016764,0.0007325394,0.0003569996,0.2509135,0.2057759,0.02095278,0.005628065,0.5082626],"study_design_scores_gemma":[0.00002363404,0.0002018919,0.0003664376,0.0000119529,0.00001632916,0.000153138,0.00003278196,0.9547491,0.03912292,0.003349413,0.001954246,0.00001806728],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0321602,0.0003378178,0.9638208,0.0001833348,0.00004024214,0.000118662,0.00005109575,0.001519143,0.001768733],"genre_scores_gemma":[0.6953861,0.000280127,0.3003331,0.0002363997,0.00005912874,0.00009902126,0.0001377258,0.000122695,0.003345823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001690423,"threshold_uncertainty_score":0.005654991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02730909917065205,"score_gpt":0.3075135664525185,"score_spread":0.2802044672818665,"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."}}