{"id":"W2771729497","doi":"10.1002/aic.16059","title":"Extracting dynamic features with switching models for process data analytics and application in soft sensing","year":2017,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Process (computing); Computer science; Representation (politics); Latent variable; Context (archaeology); Data mining; Analytics; Soft sensor; Machine learning; Probabilistic logic; Feature (linguistics); Bayesian probability; Artificial intelligence; Construct (python library)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009067138,0.0008832659,0.001035952,0.001090429,0.0003438939,0.0009437657,0.0006954528,0.0008560389,0.001140695],"category_scores_gemma":[0.003768509,0.0004669763,0.001079095,0.00138533,0.0006379429,0.001693031,0.001050162,0.001392885,0.0003389764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005586612,"about_ca_system_score_gemma":0.0006522015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001823756,"about_ca_topic_score_gemma":0.001629348,"domain_scores_codex":[0.9995259,0.0001115515,0.00004216779,0.0001345975,0.0001474127,0.00003839517],"domain_scores_gemma":[0.9988458,0.0007431449,0.0001651593,0.0001267099,0.00009445109,0.0000247835],"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":[0.0001086836,0.0001326874,0.002278718,0.0001921196,0.00006927486,0.0001830494,0.0001473495,0.6782027,0.01563855,0.02508952,0.001184086,0.2767733],"study_design_scores_gemma":[0.00000189403,0.00001254284,0.0001871355,0.000003844983,0.000004682868,0.00002114408,0.000007046227,0.9906408,0.001123579,0.007653764,0.000337413,0.000006148421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005818665,0.000166246,0.9934202,0.00007715762,0.000008823597,0.00001276024,0.00004133422,0.000206613,0.000248213],"genre_scores_gemma":[0.6244906,0.0007978417,0.372716,0.0001053401,0.00007285683,0.0001666032,0.0004806208,0.00009842066,0.001071642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001823756,"threshold_uncertainty_score":0.004795253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02134140281147947,"score_gpt":0.2913420792251734,"score_spread":0.270000676413694,"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."}}