{"id":"W4408441590","doi":"10.1109/jiot.2025.3551237","title":"QoE-Aware Volumetric Video Caching and Rendering for Mobile Extended Reality Services","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Rendering (computer graphics); Mobile telephony; Virtual reality; Augmented reality; Mobile computing; Multimedia; Computer graphics (images); Computer network; Mobile radio; Human–computer interaction","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":[],"consensus_categories":[],"category_scores_codex":[0.001012421,0.0001486396,0.000274203,0.0003805453,0.0001574971,0.000421396,0.0008930761,0.0000709614,0.00000281367],"category_scores_gemma":[0.00006375457,0.0001357964,0.0001587961,0.0002404863,0.00003095271,0.0009151996,0.0002399804,0.0003675377,8.120002e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007869973,"about_ca_system_score_gemma":0.00005374854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007688557,"about_ca_topic_score_gemma":0.00002230984,"domain_scores_codex":[0.9986739,0.00007219851,0.0004623488,0.0003004889,0.0002445131,0.0002465474],"domain_scores_gemma":[0.998915,0.0002056898,0.0003164765,0.0002689832,0.0002050317,0.00008880836],"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":[0.000570908,0.0005552952,0.01196698,0.002311521,0.00126423,0.0001522138,0.01992077,0.003898149,0.05150039,0.008011187,0.01755558,0.8822927],"study_design_scores_gemma":[0.001738929,0.00050869,0.002930247,0.001796279,0.0001014601,0.0004958413,0.0007349196,0.9638231,0.01250795,0.01141598,0.003478865,0.0004676744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3609776,0.0009595428,0.6360288,0.0003352681,0.001253381,0.0001134566,0.000002318667,0.00006219416,0.0002675043],"genre_scores_gemma":[0.995072,0.00007088779,0.003669786,0.0004179924,0.00008065344,0.000008328368,6.602754e-7,0.000007796547,0.0006719059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.959925,"threshold_uncertainty_score":0.5537617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01475399568406212,"score_gpt":0.272279712687759,"score_spread":0.2575257170036969,"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."}}