{"id":"W4388430577","doi":"10.1109/jiot.2023.3330411","title":"Digital Twin Model Selection for Feature Accuracy","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Knapsack problem; Feature selection; Generalization; Feature (linguistics); Constraint (computer-aided design); Rounding; Selection (genetic algorithm); Mathematical optimization; Approximation algorithm; Linear programming relaxation; Relaxation (psychology); Integer programming; Algorithm; Artificial intelligence; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001577712,0.001316008,0.001342928,0.0008360389,0.0006817634,0.001413619,0.001554681,0.0008392579,0.004537428],"category_scores_gemma":[0.005482355,0.0006652066,0.001316738,0.001402835,0.0006778871,0.001928882,0.001728641,0.00174354,0.0005660081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001276446,"about_ca_system_score_gemma":0.001910006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006324814,"about_ca_topic_score_gemma":0.005807367,"domain_scores_codex":[0.9985591,0.000479659,0.00006487493,0.0002321993,0.0004798731,0.0001844199],"domain_scores_gemma":[0.9981807,0.001048783,0.0001636403,0.0002151762,0.0003141422,0.00007757574],"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":[0.00007640315,0.00003403252,0.0004803914,0.00005188856,0.000017806,0.00006765297,0.00003222679,0.9608847,0.001135104,0.01060449,0.0009465672,0.0256687],"study_design_scores_gemma":[0.000005483736,0.00002006012,0.00003636822,0.000003043546,0.000004746909,0.00001576379,0.000009313757,0.9956833,0.0004493789,0.003264372,0.0005045619,0.000003602748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01563235,0.0002082423,0.9802212,0.00016022,0.00004067639,0.00006313681,0.0001085339,0.0003611423,0.003204569],"genre_scores_gemma":[0.6401534,0.0003899144,0.3527154,0.0001588967,0.0000442262,0.000282686,0.0006675514,0.0002748796,0.005313033],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006324814,"threshold_uncertainty_score":0.01517922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02303042428704357,"score_gpt":0.2579058418164894,"score_spread":0.2348754175294458,"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."}}