{"id":"W4380360158","doi":"10.48550/arxiv.2306.05946","title":"Digital Twin-Assisted Resource Demand Prediction for Multicast Short Video Streaming","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Peng Cheng Laboratory","keywords":"Multicast; Computer science; Cluster analysis; Construct (python library); Computer network; Resource (disambiguation); Duration (music); Scheme (mathematics); Video on demand; On demand; Multimedia; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007172812,0.0005302603,0.0007501674,0.0005401884,0.0004461678,0.0004302914,0.001271881,0.0004034736,0.0006422921],"category_scores_gemma":[0.002639404,0.0002249676,0.0002486894,0.0005982858,0.0003664098,0.00140394,0.0008475173,0.0007222896,0.0001775715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005900948,"about_ca_system_score_gemma":0.0005740033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005053902,"about_ca_topic_score_gemma":0.005095045,"domain_scores_codex":[0.9994548,0.0001055639,0.00003722552,0.0001603089,0.0001709944,0.00007116982],"domain_scores_gemma":[0.998865,0.0004145375,0.0001540027,0.0002127731,0.0002552888,0.00009842599],"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":[0.0007775658,0.0002497984,0.01210109,0.00009408381,0.00007569261,0.0002236881,0.0003161805,0.4832004,0.03483473,0.006588702,0.002548456,0.4589896],"study_design_scores_gemma":[0.000003496241,0.00002293122,0.0003291834,0.000001307884,0.000003766541,0.00002295664,0.00001034669,0.9969393,0.001750423,0.0007333246,0.0001780159,0.000004927811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08179045,0.0001870544,0.9163444,0.0001229042,0.0000437847,0.00003868916,0.0001081228,0.0005948779,0.000769711],"genre_scores_gemma":[0.919302,0.00007903518,0.07956221,0.00005860371,0.00003356792,0.00003095109,0.0001720672,0.00002371639,0.0007378877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005053902,"threshold_uncertainty_score":0.01004899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.133415315828517,"score_gpt":0.2440335401716902,"score_spread":0.1106182243431731,"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."}}