{"id":"W4411783611","doi":"10.1021/acs.jcim.5c00809","title":"A Diagnosis-Based Siamese Network for Fault Detection Through Transfer Learning","year":2025,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Agência Nacional do Petróleo, Gás Natural e Biocombustíveis; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Embedding; Transfer of learning; Similarity (geometry); Fault (geology); Fault detection and isolation; Task (project management); Artificial intelligence; Anomaly detection; Pattern recognition (psychology); Machine learning; Set (abstract data type); Artificial neural network; Data mining; Feature vector; Feature (linguistics); Image (mathematics); Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009105348,0.0008221905,0.0006164814,0.000828762,0.000324157,0.000619919,0.001082001,0.0009532033,0.001698449],"category_scores_gemma":[0.002529782,0.0002961566,0.0006509601,0.0006120413,0.0006917426,0.001278656,0.0008782441,0.00139335,0.0004081447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001026066,"about_ca_system_score_gemma":0.001118648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007979486,"about_ca_topic_score_gemma":0.006734625,"domain_scores_codex":[0.9996694,0.00007155591,0.00002112612,0.0001165395,0.00008339392,0.00003800125],"domain_scores_gemma":[0.9991632,0.00034738,0.00008916196,0.00007898619,0.0002794792,0.00004181895],"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":[0.00009389114,0.0001131594,0.001832139,0.00004082113,0.0000675923,0.00009591031,0.00004775765,0.8603768,0.004031403,0.00604115,0.001361649,0.1258977],"study_design_scores_gemma":[0.000001271118,0.00001000017,0.00006102584,9.158817e-7,0.000002320181,0.000008393475,0.00000111608,0.9986077,0.0003569696,0.000861619,0.00008687784,0.00000184579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03731879,0.0003420716,0.9593362,0.0003705322,0.0000737411,0.00005216511,0.00009344784,0.000940517,0.001472636],"genre_scores_gemma":[0.8710816,0.0002350427,0.1236885,0.0002136032,0.00006322263,0.0001068777,0.0003458298,0.00005175231,0.004213532],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007979486,"threshold_uncertainty_score":0.0158661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01087719923405851,"score_gpt":0.2394611774862231,"score_spread":0.2285839782521646,"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."}}