{"id":"W4385694459","doi":"10.1109/bsc57238.2023.10201459","title":"Optimum Digital Twin Response Time for Time-Sensitive Applications","year":2023,"lang":"en","type":"article","venue":"","topic":"Age of Information Optimization","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Replica; Markov decision process; Response time; Process (computing); Markov process; Stochastic process; Markov chain; Channel (broadcasting); Real-time computing; Computer network; Machine learning; Mathematics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003221079,0.00008392386,0.00008080989,0.0001784581,0.0001343425,0.0002794952,0.0003348623,0.00004317515,0.00004142658],"category_scores_gemma":[0.0001283692,0.00008076445,0.00004905578,0.0006594311,0.00002470973,0.001360611,0.0001516082,0.00003190352,0.01034153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003907331,"about_ca_system_score_gemma":0.00006437887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.502034e-7,"about_ca_topic_score_gemma":3.116466e-8,"domain_scores_codex":[0.9992718,0.0000187944,0.0001905216,0.0001843951,0.0001543532,0.000180182],"domain_scores_gemma":[0.9989617,0.0003848216,0.0000652146,0.0003361445,0.0001895662,0.00006258962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004593987,0.0001872105,0.00001835463,0.00003860523,0.0001134855,0.00001328585,0.004571749,0.1500692,0.005617895,0.1208533,0.5831712,0.1348864],"study_design_scores_gemma":[0.0002025969,0.00004354218,0.00008492373,0.000003024928,0.000001552445,0.000004830155,0.00003881498,0.9750483,0.001112988,0.0006379301,0.02269903,0.0001224399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000451737,8.063981e-7,0.9773393,0.002213076,0.00002809372,0.0005344784,0.00002638326,0.0008704361,0.01853569],"genre_scores_gemma":[0.04456738,0.00000347344,0.7372683,0.00152097,0.0001558504,0.0006442843,0.0006188659,0.00004689526,0.2151739],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8249792,"threshold_uncertainty_score":0.990429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008286575484845684,"score_gpt":0.2358596267501099,"score_spread":0.2275730512652642,"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."}}