{"id":"W3184235966","doi":"10.23919/acc50511.2021.9482953","title":"Comprehensive Monitoring with Incremental Slow Feature Analysis","year":2021,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Feature (linguistics); Computer science; Process (computing); Curse of dimensionality; Covariance; Scheme (mathematics); Data mining; Feature selection; Work in process; Algorithm; Artificial intelligence; Mathematics; Engineering; Statistics","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":[0.0006724285,0.0007947384,0.0007730651,0.001543465,0.0004961431,0.0006104745,0.000989542,0.0003465391,0.0008513238],"category_scores_gemma":[0.002377974,0.0003173799,0.0005457105,0.001010253,0.0004351199,0.001521461,0.001056472,0.0007535241,0.0002677357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004308266,"about_ca_system_score_gemma":0.0008672832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004733967,"about_ca_topic_score_gemma":0.005071977,"domain_scores_codex":[0.9994073,0.0000691625,0.00003835833,0.0001540691,0.0002744811,0.00005661721],"domain_scores_gemma":[0.9988225,0.0002801096,0.0001683419,0.0002158796,0.0004570232,0.00005618676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003819031,0.0001606305,0.008062787,0.00009348163,0.00009267651,0.000177307,0.0002095344,0.1180169,0.05480919,0.005528065,0.002653074,0.8098144],"study_design_scores_gemma":[0.00001469008,0.0001527488,0.002630787,0.000004236031,0.00002058118,0.00007581036,0.00001551643,0.9822566,0.01125784,0.001962002,0.001584015,0.00002517123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04184848,0.000153792,0.9559817,0.00005834654,0.0000371307,0.00006196769,0.00008117581,0.001053283,0.0007241279],"genre_scores_gemma":[0.6878124,0.0001079922,0.3099774,0.00004335488,0.00004834493,0.0001083615,0.0002553927,0.00007562551,0.001571152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004733967,"threshold_uncertainty_score":0.009412825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007637082610125395,"score_gpt":0.2115078303086866,"score_spread":0.2038707476985612,"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."}}