{"id":"W4408657814","doi":"10.1016/j.conengprac.2025.106322","title":"A novel automated soft sensor design tool for industrial applications based on machine learning","year":2025,"lang":"en","type":"article","venue":"Control Engineering Practice","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"British Columbia Dairy Association; University of British Columbia","funders":"Mitacs","keywords":"Soft sensor; Soft robotics; Computer science; Engineering; Manufacturing engineering; Artificial intelligence; Control engineering; Robot; Process (computing); Operating system","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":[0.0005061011,0.0009125707,0.0006953673,0.000650441,0.0003675345,0.0008959157,0.00133035,0.0008479079,0.008697938],"category_scores_gemma":[0.001503331,0.0005079706,0.0005265396,0.0002947466,0.0003340094,0.0007545847,0.0007668535,0.001077699,0.002010064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000322121,"about_ca_system_score_gemma":0.0007637838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007240505,"about_ca_topic_score_gemma":0.001800867,"domain_scores_codex":[0.9993819,0.00007411165,0.0000346545,0.00009809309,0.0003743504,0.00003692325],"domain_scores_gemma":[0.9992551,0.0003023344,0.00009481656,0.0001166434,0.0002042859,0.00002676108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003496693,0.0002674756,0.000760528,0.0004778282,0.0001328169,0.0004355956,0.0001412087,0.1573656,0.1075663,0.0277706,0.0137528,0.6909796],"study_design_scores_gemma":[0.00004057062,0.0001368395,0.0002086084,0.00002860931,0.000021919,0.0002231895,0.00001218821,0.9449718,0.03336781,0.006270701,0.01469508,0.00002266323],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001348421,0.00004263703,0.9940257,0.00003927278,0.00003748643,0.00003619648,0.00003350122,0.003430598,0.001006032],"genre_scores_gemma":[0.1390797,0.0001140087,0.8535023,0.0002222442,0.00005930807,0.0002094428,0.0001795035,0.0004478162,0.006185712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008697938,"threshold_uncertainty_score":0.02909744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01470293595804819,"score_gpt":0.2450216145987328,"score_spread":0.2303186786406846,"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."}}