{"id":"W4401748508","doi":"10.1109/jiot.2024.3446801","title":"Digital-Twin-Empowered Resource Allocation for On-Demand Collaborative Sensing","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Age of Information Optimization","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo; Carleton University","funders":"","keywords":"Computer science; Resource allocation; Resource management (computing); Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005921137,0.0005454729,0.0005270191,0.0003069671,0.0004971867,0.0007146189,0.001237323,0.0005841762,0.001509503],"category_scores_gemma":[0.001427045,0.0002334174,0.0003339989,0.0003972084,0.0006872604,0.001607264,0.001678633,0.0006530141,0.000197038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005995104,"about_ca_system_score_gemma":0.0006544923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0012731,"about_ca_topic_score_gemma":0.002101649,"domain_scores_codex":[0.9995002,0.0001172402,0.00002472815,0.0001301378,0.0001643797,0.00006325242],"domain_scores_gemma":[0.9996417,0.0001315024,0.00004324167,0.00006576221,0.00008232917,0.00003545684],"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":[0.0002358661,0.0001786885,0.001190813,0.0001634028,0.00005479851,0.0002614379,0.0001871693,0.7390019,0.05595463,0.07825913,0.002352259,0.12216],"study_design_scores_gemma":[0.000004198846,0.00003296962,0.00006036806,0.000002876938,0.000003706345,0.00003374864,0.00001421489,0.9916829,0.002972241,0.004502647,0.0006830607,0.000007112156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01673501,0.00008658964,0.9799924,0.000092813,0.00004176171,0.000028827,0.00002358662,0.0001016264,0.002897368],"genre_scores_gemma":[0.882053,0.0001299645,0.1149684,0.0001134732,0.00003225491,0.00006880561,0.00004295627,0.00003583179,0.002555284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001509503,"threshold_uncertainty_score":0.005049825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01012648979775106,"score_gpt":0.2518374607946808,"score_spread":0.2417109709969298,"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."}}