{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001439567,0.0001156777,0.0001345353,0.0001877794,0.0000288543,0.0002596293,0.0002154587,0.0001166987,0.00001274523],"category_scores_gemma":[0.00007483321,0.0001114156,0.0001313913,0.0001710477,0.00001676888,0.00197527,0.000009324375,0.0004040254,0.00003180368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007204726,"about_ca_system_score_gemma":0.00002180285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.658045e-7,"about_ca_topic_score_gemma":2.303495e-7,"domain_scores_codex":[0.9992283,0.000003101371,0.0003001157,0.00006936448,0.0001942227,0.0002048894],"domain_scores_gemma":[0.9996109,0.0000746304,0.00008067118,0.00005702981,0.0001068764,0.00006988535],"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":[0.00005472925,0.00002746741,0.0002592541,0.0001901177,0.0001999308,0.000003926655,0.002353509,0.5911449,0.007415158,0.0003584359,0.3578565,0.04013611],"study_design_scores_gemma":[0.0003986396,0.00005799068,0.00003274801,0.0001529017,0.00001256303,0.0001594695,0.0001223306,0.9501047,0.0385531,0.002586111,0.007656028,0.0001634714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.482842,0.00003909728,0.4713509,0.000380713,0.002562455,0.0002505024,0.0000904536,0.0007168978,0.04176696],"genre_scores_gemma":[0.9949817,0.00001168395,0.001322986,0.00003843471,0.0001808127,0.000006754144,0.00001305678,0.00003558734,0.003408974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5121397,"threshold_uncertainty_score":0.4543397,"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."}}