{"id":"W4390891847","doi":"10.1016/j.conengprac.2024.105850","title":"Relevance vector machine with hybrid kernel-based soft sensor via data augmentation for incomplete output data in sintering process","year":2024,"lang":"en","type":"article","venue":"Control Engineering Practice","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Soft sensor; Computer science; Kernel (algebra); Support vector machine; Data mining; Key (lock); Relevance vector machine; Process (computing); Fuzzy logic; Relevance (law); Cluster analysis; Artificial intelligence; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002241958,0.0003252088,0.0003172164,0.0001541866,0.00003102834,0.0001171036,0.0008238746,0.00007678557,0.000003973139],"category_scores_gemma":[0.002307025,0.000319478,0.00002353053,0.0002863899,0.00003178909,0.001514775,0.0001186482,0.0005092309,0.000012752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002041491,"about_ca_system_score_gemma":0.00001586295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001109225,"about_ca_topic_score_gemma":0.000009637701,"domain_scores_codex":[0.9983383,0.00001302133,0.0003604547,0.0006494666,0.000212678,0.0004260475],"domain_scores_gemma":[0.9969349,0.00170144,0.00006174403,0.001179419,0.00005991916,0.00006259362],"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":[0.0001941802,0.0000240446,0.00001113283,0.001010153,0.0001190069,0.0001023138,0.00002691517,0.9410771,0.04633518,0.00002179143,0.0000956173,0.01098252],"study_design_scores_gemma":[0.001160361,0.00004971788,0.00001648516,0.0003230678,0.00009960452,0.0000615918,0.00002584108,0.9618045,0.01123472,0.00002339685,0.02482894,0.0003717474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00564764,0.001888237,0.9881506,0.0007395389,0.0003450379,0.0006121768,0.0005917663,0.001998524,0.0000264705],"genre_scores_gemma":[0.9545254,0.00003653415,0.04456764,0.00005471151,0.0001171918,0.000113529,0.0004352824,0.000134945,0.00001478831],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9488778,"threshold_uncertainty_score":0.9999257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02220471153609321,"score_gpt":0.2844555579133419,"score_spread":0.2622508463772487,"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."}}