{"id":"W4405403261","doi":"10.23977/jemm.2024.090304","title":"Health Prediction of Integrated Die-Casting Machine Driven by Digital Twin and CNN-LSTM","year":2024,"lang":"en","type":"article","venue":"Journal of Engineering Mechanics and Machinery","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Die casting; Die (integrated circuit); Computer science; Artificial intelligence; Casting; Engineering drawing; Engineering; Materials science; Composite material; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002212414,0.0001557254,0.0002555719,0.0002227826,0.00001943357,0.000122335,0.00006279738,0.00007192855,0.000004381883],"category_scores_gemma":[0.0000400262,0.0001362086,0.00005344164,0.0001430206,0.000006456912,0.0005668671,0.00001852074,0.0004172323,3.885066e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005636689,"about_ca_system_score_gemma":0.00002560839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004033399,"about_ca_topic_score_gemma":4.158345e-7,"domain_scores_codex":[0.999099,0.000005548517,0.0005136586,0.00007884306,0.0001550687,0.0001479146],"domain_scores_gemma":[0.9996566,0.00005993515,0.0000649963,0.0000546009,0.00003748706,0.0001264125],"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":[0.00006791677,0.0001567705,0.000937734,0.007420846,0.001381446,0.0001116266,0.002326553,0.5412952,0.05665239,0.013871,0.007387595,0.3683909],"study_design_scores_gemma":[0.0002526162,0.0001636534,0.00006687375,0.0007490282,0.00002159815,0.0002479693,0.00006864675,0.9908805,0.0005404482,0.0001898573,0.006697387,0.0001214473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2226314,0.01591367,0.7576415,0.0002283574,0.001764676,0.0001560152,0.0008141213,0.0002918655,0.0005584501],"genre_scores_gemma":[0.9980343,0.0009474054,0.0008483091,0.000007345121,0.00007605224,0.000001241809,0.00002658833,0.00003696509,0.00002181514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7754029,"threshold_uncertainty_score":0.5554426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008219200013687209,"score_gpt":0.1997169907000796,"score_spread":0.1914977906863924,"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."}}