{"id":"W4383532443","doi":"10.1002/cjce.25041","title":"Process operation performance assessment of electro‐fused magnesium furnace based on deep auto‐encoder transfer generative adversarial network","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Process (computing); Domain (mathematical analysis); Encoder; Generative adversarial network; Transfer of learning; Generative grammar; Transfer (computing); Artificial intelligence; Algorithm; Data mining; Deep learning; Mathematics","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":[0.0004071075,0.0001816429,0.0002382388,0.000183416,0.0000638952,0.00003134222,0.0002870088,0.00009264953,0.00002294351],"category_scores_gemma":[0.0000670102,0.0001544007,0.0000702248,0.0004426774,0.00004220498,0.0001322804,0.000004635701,0.0005248375,9.997325e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003728823,"about_ca_system_score_gemma":0.0003872458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003918462,"about_ca_topic_score_gemma":0.00003785694,"domain_scores_codex":[0.9989259,0.00002147539,0.0003439268,0.0000950351,0.0002496517,0.000364036],"domain_scores_gemma":[0.9993995,0.0001012736,0.00004380658,0.0001388778,0.0001329493,0.0001835804],"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":[0.000009708205,0.00000291143,0.0002294249,0.00007051574,0.00002366153,0.000008713446,0.0001368665,0.8596144,0.1392629,0.0004782268,0.00007196616,0.00009062544],"study_design_scores_gemma":[0.0003152812,0.00009518999,0.0006837244,0.0001083951,0.00002821994,0.00001794274,0.000004572167,0.8286219,0.1695649,0.0003925028,0.00001060538,0.0001567403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9182781,0.00004904849,0.08042654,0.0002056011,0.0003313748,0.0001888803,0.000005314419,0.0001631947,0.0003519238],"genre_scores_gemma":[0.9743195,0.000002059554,0.02529082,0.00002857601,0.0002941265,0.00001245676,0.000005082815,0.0000460328,0.000001310876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0560414,"threshold_uncertainty_score":0.6296278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00830094556972298,"score_gpt":0.2171029025497848,"score_spread":0.2088019569800618,"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."}}