{"id":"W3009842943","doi":"10.1002/cjce.23738","title":"Batch process monitoring using multiway Laplacian autoencoders","year":2020,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Laplacian matrix; Autoencoder; Laplace operator; Regularization (linguistics); Artificial intelligence; Computer science; Pattern recognition (psychology); Nonlinear system; Graph; Artificial neural network; Batch processing; Mathematics; Theoretical computer science","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":[0.0001170009,0.0001390216,0.0002031657,0.00007525217,0.00005430752,0.00007135795,0.000262739,0.00007832913,0.0000136724],"category_scores_gemma":[0.0001172038,0.0001171538,0.0000878024,0.0002344406,0.00002044563,0.0001235563,0.00000436092,0.0004572514,0.000004927271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002090218,"about_ca_system_score_gemma":0.0001295863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002361898,"about_ca_topic_score_gemma":0.00003437276,"domain_scores_codex":[0.9991706,0.000007907536,0.0002977331,0.00006798131,0.0001643536,0.0002914035],"domain_scores_gemma":[0.9992184,0.0000300443,0.00004671341,0.00007782466,0.00006015219,0.000566862],"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":[0.000004003907,7.494914e-7,0.0001449448,0.00006063116,0.00005243007,0.00002781029,0.001018622,0.8734129,0.124851,0.000007247609,0.00004966092,0.000369956],"study_design_scores_gemma":[0.0002730185,0.00001092021,0.00002393267,0.00009609797,0.00002192345,0.0001060073,0.0001290319,0.9175079,0.08052468,0.000005321931,0.001147564,0.0001535626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920574,0.0006365505,0.005499923,0.0004680124,0.0009876672,0.00008560672,0.000003226358,0.00009942245,0.0001621652],"genre_scores_gemma":[0.9988586,0.000001094774,0.0003374221,0.00004051978,0.0007205925,0.000001795679,1.81722e-7,0.00003713272,0.000002674205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04432635,"threshold_uncertainty_score":0.4777391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01379927513646515,"score_gpt":0.2048883618956183,"score_spread":0.1910890867591532,"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."}}