{"id":"W3160532556","doi":"10.1002/cjce.24149","title":"Locality‐preserving data modelling and its application in fault classification","year":2021,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Linear discriminant analysis; Locality; Neighbourhood (mathematics); Computer science; Pattern recognition (psychology); Artificial intelligence; Discriminant; Data mining; Embedding; Benchmark (surveying); Fault (geology); Process (computing); Machine learning; Mathematics; Geography; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001766189,0.0004203399,0.0007222707,0.001125303,0.0003619332,0.0006381639,0.0006884643,0.0004940797,0.000593896],"category_scores_gemma":[0.004141236,0.0002188215,0.0008377319,0.001064893,0.0009446928,0.001112072,0.0008790704,0.0007332346,0.0002380936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005296463,"about_ca_system_score_gemma":0.0005093077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001986566,"about_ca_topic_score_gemma":0.001294642,"domain_scores_codex":[0.9991085,0.0003221291,0.00007266927,0.0001832708,0.0002655459,0.00004779632],"domain_scores_gemma":[0.9975211,0.001198916,0.000338297,0.0004435552,0.0004338172,0.00006425252],"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":[0.0002692871,0.0001379675,0.005933785,0.0001767609,0.0001176529,0.0001861359,0.0002230402,0.561233,0.03927619,0.01498206,0.0007548751,0.3767093],"study_design_scores_gemma":[0.000002676845,0.00003691904,0.0004458284,0.000003410852,0.000006325597,0.00002907453,0.00001011878,0.9926876,0.003368985,0.003096583,0.0003045571,0.00000790907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02996175,0.0001638642,0.9692442,0.00008474691,0.00001230644,0.00001576876,0.00002659213,0.0002222957,0.0002684778],"genre_scores_gemma":[0.8452291,0.0001641774,0.1536982,0.00002962924,0.00003304614,0.00005433927,0.00009468462,0.00003109246,0.0006656717],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001986566,"threshold_uncertainty_score":0.009340644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02643263524871637,"score_gpt":0.2144773929795187,"score_spread":0.1880447577308023,"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."}}