{"id":"W2902166189","doi":"10.1109/tii.2018.2880968","title":"Deep Learning of Complex Batch Process Data and Its Application on Quality Prediction","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"China Scholarship Council; Ministry of Science and Technology, Taiwan; National Natural Science Foundation of China","keywords":"Computer science; Process (computing); Quality (philosophy); Artificial intelligence; Encoder; Feature (linguistics); Deep learning; Face (sociological concept); Machine learning; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003233242,0.000121325,0.0001828245,0.0001208225,0.0001460898,0.00002918941,0.0001459969,0.0001785816,0.00002695452],"category_scores_gemma":[0.00002063426,0.0001213754,0.00002395735,0.0002414717,0.00003740536,0.0003273684,0.000001346483,0.0003463296,0.00003573536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004370276,"about_ca_system_score_gemma":0.00001577982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001866502,"about_ca_topic_score_gemma":0.00002013114,"domain_scores_codex":[0.9989367,0.00003896437,0.0005811068,0.00009151632,0.0002256629,0.0001260359],"domain_scores_gemma":[0.9993882,0.00007282017,0.0001266386,0.0002644581,0.00008634362,0.00006153165],"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":[0.0003808575,0.0001333878,0.00008572414,0.0004282112,0.0002252979,1.103184e-7,0.004697899,0.6849095,0.004421216,0.00011624,0.0003519046,0.3042497],"study_design_scores_gemma":[0.0009044121,0.0002135178,0.00004520827,0.00004145097,0.00002596165,0.000002945904,0.001028382,0.9854226,0.008956938,0.000005557476,0.003245948,0.0001071515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2350384,0.000006682538,0.760871,0.00002627723,0.0008422945,0.0005712727,0.0001604605,0.0003241459,0.002159484],"genre_scores_gemma":[0.9996417,0.00001484249,0.00004509879,0.00001782026,0.0001706691,0.00003672334,0.00002865446,0.00001307074,0.0000314568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7646033,"threshold_uncertainty_score":0.4949544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0763634063340626,"score_gpt":0.3046590855269437,"score_spread":0.2282956791928811,"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."}}