{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004243294,0.0003493157,0.0003709975,0.0003063865,0.0002805584,0.0005544905,0.001332805,0.0002896749,0.000003551268],"category_scores_gemma":[0.0001183762,0.0004113635,0.0003222277,0.0004751793,0.00009030643,0.0008192749,0.001950229,0.000420088,0.00004139525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003098231,"about_ca_system_score_gemma":0.000173398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004550008,"about_ca_topic_score_gemma":0.0000303248,"domain_scores_codex":[0.9975217,0.0001094759,0.0003483658,0.001376359,0.0001722935,0.0004717998],"domain_scores_gemma":[0.9978085,0.0004011211,0.0001862645,0.001213828,0.0001971053,0.0001932041],"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":[0.0007294857,0.00263027,0.0578203,0.003007044,0.003087148,0.003301474,0.005049698,0.6030661,0.0009086209,0.1522126,0.0265191,0.1416681],"study_design_scores_gemma":[0.0007997449,0.0001507779,0.01154318,0.0002844859,0.0001596235,0.000008516939,0.000461964,0.9743556,0.0002436386,0.006369127,0.004976025,0.0006472986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06226537,0.00001635617,0.9342051,0.0001836566,0.0005314046,0.0006333991,0.000185888,0.0006664621,0.001312353],"genre_scores_gemma":[0.9916117,0.00001935332,0.002908157,0.00006831287,0.0001742615,0.000007444099,0.0001919744,0.00003506478,0.004983721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9312969,"threshold_uncertainty_score":0.9998338,"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."}}