{"id":"W4408528538","doi":"10.1002/cjce.25676","title":"Dynamic soft sensor modelling based on data imputation and spatiotemporal attention","year":2025,"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":"","funders":"Natural Science Foundation of Gansu Province; National Natural Science Foundation of China","keywords":"Computer science; Soft sensor; Imputation (statistics); Artificial intelligence; Data mining; Machine learning; Missing data","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001219624,0.0006793606,0.0007371784,0.000492621,0.0002821677,0.0006891054,0.001386194,0.0007965554,0.001146385],"category_scores_gemma":[0.003432888,0.0004550935,0.0008788861,0.0005881167,0.0007717792,0.001165276,0.001254447,0.001493593,0.0001821628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008199622,"about_ca_system_score_gemma":0.0007097945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008196185,"about_ca_topic_score_gemma":0.00568893,"domain_scores_codex":[0.9995782,0.0001042581,0.00002561352,0.0001415205,0.00009106071,0.00005933101],"domain_scores_gemma":[0.9988359,0.0006582754,0.0001885082,0.00009297989,0.0001817185,0.00004267421],"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":[0.00003607983,0.0000185966,0.0006572574,0.00002089818,0.00002563191,0.00005634496,0.00003647666,0.978168,0.001279413,0.005089518,0.0001719517,0.01443984],"study_design_scores_gemma":[6.177924e-7,0.000003653392,0.00005850708,0.000001052278,0.000001765523,0.000003144229,0.000001047085,0.9987458,0.0001964644,0.0009521599,0.00003420123,0.000001521686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02697238,0.0001410782,0.9715633,0.0001939249,0.00003020303,0.00001633322,0.00005919753,0.0002587388,0.0007648877],"genre_scores_gemma":[0.9567984,0.0001584299,0.0403177,0.0001169197,0.00003744575,0.0000541098,0.0001390561,0.00004019389,0.00233778],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008196185,"threshold_uncertainty_score":0.01629692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008133908402925454,"score_gpt":0.1987511138851056,"score_spread":0.1906172054821801,"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."}}