{"id":"W4382701145","doi":"10.1016/j.iot.2023.100753","title":"Real-time Mixed Reality (MR) and Artificial Intelligence (AI) object recognition integration for digital twin in Industry 4.0","year":2023,"lang":"en","type":"article","venue":"Internet of Things","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Innovation and Technology Fund; Hong Kong Polytechnic University","keywords":"Computer science; Digitization; Big data; Cloud computing; Artificial intelligence; Augmented reality; Object (grammar); Data mining; Computer vision; Operating system","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":[0.001281246,0.0004080988,0.000329499,0.001064605,0.000212879,0.001807302,0.001042316,0.001033176,0.00995504],"category_scores_gemma":[0.0009210364,0.0002891578,0.0004244741,0.0006455111,0.0003862398,0.001703742,0.001208589,0.0006555514,0.003777098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004191342,"about_ca_system_score_gemma":0.0005581995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002078491,"about_ca_topic_score_gemma":0.001981389,"domain_scores_codex":[0.9994546,0.00007628841,0.00002938477,0.00007782126,0.0002928262,0.00006908205],"domain_scores_gemma":[0.9995566,0.00006106659,0.00002754879,0.00008095418,0.0002311646,0.00004267463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000525705,0.0002759727,0.002421735,0.0004023584,0.00008136779,0.0003234544,0.0003535361,0.009677877,0.1574206,0.0307073,0.01893543,0.7788748],"study_design_scores_gemma":[0.00007424056,0.001092809,0.01028365,0.0003265816,0.0002310685,0.001335103,0.0004701622,0.4223027,0.2653811,0.01604998,0.2822863,0.0001664724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03901871,0.003485923,0.8962172,0.001316689,0.0005026523,0.0001464281,0.0003117868,0.01113086,0.04786981],"genre_scores_gemma":[0.4528905,0.003377394,0.4820348,0.000725737,0.0002377638,0.00009296861,0.0009900449,0.0006722412,0.05897848],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00995504,"threshold_uncertainty_score":0.03330296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04573086442547186,"score_gpt":0.2768123001782136,"score_spread":0.2310814357527418,"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."}}