{"id":"W4391305525","doi":"10.1109/ojcoms.2024.3359188","title":"Warm and Cold Start Quantum Annealing for Metaverse Resource Optimization","year":2024,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quantum annealing; Annealing (glass); Metaverse; Computer science; Quantum; Materials science; Physics; Human–computer interaction; Quantum mechanics; Quantum computer; Virtual reality; Metallurgy","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":[0.001873957,0.00008386659,0.000149566,0.00003300018,0.0007737273,0.0008223898,0.003603921,0.00004281221,6.672904e-7],"category_scores_gemma":[0.00005554364,0.00006115704,0.0002000985,0.0003606905,0.00008971198,0.000678563,0.00117805,0.0002918309,0.000001112387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007387748,"about_ca_system_score_gemma":0.0001700309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001022114,"about_ca_topic_score_gemma":8.99612e-7,"domain_scores_codex":[0.9990962,0.0001656544,0.0003343782,0.0001185938,0.000146948,0.0001382503],"domain_scores_gemma":[0.998113,0.0005205577,0.0002187114,0.0008950059,0.0001987983,0.00005387985],"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":[0.0000295399,0.0002138126,0.0001422212,0.0001689919,0.0008141042,0.000003009144,0.03557399,0.1092807,0.003064543,0.03408934,0.7966999,0.01991985],"study_design_scores_gemma":[0.0002030897,0.00003266777,0.00001070164,0.0001484029,0.00004232103,0.00002989585,0.000233468,0.7734644,0.0003888245,0.001193225,0.2241784,0.00007460683],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003126649,0.003198721,0.96348,0.02588354,0.003338204,0.0004234838,0.000002058757,0.00003913827,0.0005081916],"genre_scores_gemma":[0.1473126,0.0009319933,0.8493112,0.001150314,0.000672814,0.00001437841,0.000002460573,0.00002821495,0.0005760757],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6641837,"threshold_uncertainty_score":0.7930325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07834750480783226,"score_gpt":0.3276060493852609,"score_spread":0.2492585445774287,"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."}}