{"id":"W4297887792","doi":"10.36227/techrxiv.21113374","title":"Nonlinear Semi-supervised Inference Networks for the Extraction of Slow Oscillating Features","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Collinearity; Computer science; Inference; Data mining; Process (computing); Nonlinear system; Autoregressive model; Imputation (statistics); Measure (data warehouse); Feature extraction; Machine learning; Variable (mathematics); Feature selection; Artificial intelligence; Pattern recognition (psychology); Missing data; Mathematics; Statistics","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":[0.001843901,0.0007416126,0.0008287959,0.0004768794,0.0002927715,0.0005293932,0.001272215,0.0007796457,0.001041697],"category_scores_gemma":[0.004912557,0.000483307,0.0006067635,0.0004712883,0.0005619911,0.0008443169,0.0006609323,0.001295413,0.0003386828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006030035,"about_ca_system_score_gemma":0.0007829566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004982724,"about_ca_topic_score_gemma":0.005405587,"domain_scores_codex":[0.9994102,0.0002342743,0.00003844172,0.0001587764,0.0001068398,0.00005150913],"domain_scores_gemma":[0.9974131,0.001667046,0.0003040257,0.0001779669,0.0003935788,0.00004437188],"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":[0.000158389,0.0000993781,0.001048203,0.00007804175,0.00007903438,0.00006869581,0.0000903894,0.8536997,0.003841088,0.003649508,0.0007962916,0.1363913],"study_design_scores_gemma":[0.000001248451,0.000005215649,0.00004812028,0.000001091674,0.00000162627,0.000001961622,0.000001017208,0.999127,0.0002729668,0.000507009,0.00003148945,0.000001233215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01591975,0.0001411781,0.9830568,0.00006205633,0.0000133551,0.00002773812,0.00003945485,0.000425871,0.0003137243],"genre_scores_gemma":[0.7080383,0.0001782648,0.2883535,0.0001136557,0.00005659428,0.0001786637,0.0004317772,0.0001101679,0.002539092],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004982724,"threshold_uncertainty_score":0.009907424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01653062301512504,"score_gpt":0.2667255252700513,"score_spread":0.2501949022549262,"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."}}