{"id":"W4249218604","doi":"10.1109/.2001.980441","title":"Detecting leaks and sensor biases by recursive identification with forgetting factors","year":2002,"lang":"en","type":"article","venue":"Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228)","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Boiler (water heating); Forgetting; Leak; Leak detection; Leakage (economics); Computer science; Engineering; Waste management; Environmental 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":[0.001088369,0.001191569,0.0009062173,0.0004609606,0.0002144507,0.0006146898,0.0007803913,0.0009481303,0.0003832889],"category_scores_gemma":[0.004734784,0.0006524246,0.0006656522,0.0003881906,0.0006430777,0.001579198,0.0006416292,0.001166748,0.0002322172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000334547,"about_ca_system_score_gemma":0.0005810651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003596366,"about_ca_topic_score_gemma":0.003200389,"domain_scores_codex":[0.9993951,0.0001499487,0.00004768153,0.0001421207,0.0001878346,0.0000773083],"domain_scores_gemma":[0.9981949,0.0009629945,0.0003063957,0.0002335923,0.0002700835,0.00003202982],"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":[0.000535666,0.0001435953,0.004224899,0.0003082699,0.0002012855,0.0002840406,0.0003829813,0.6316684,0.06142412,0.004707975,0.0007135377,0.2954053],"study_design_scores_gemma":[0.00001622418,0.0001031881,0.0006788655,0.000009064337,0.00003011981,0.00007331112,0.00001034636,0.9892883,0.00775128,0.001653416,0.0003626425,0.00002312802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02704901,0.0002282161,0.9719759,0.00004553048,0.00001653901,0.00001242007,0.00001102033,0.0005178236,0.0001434907],"genre_scores_gemma":[0.7510462,0.0007274465,0.2465053,0.0001084528,0.00006606168,0.00005920065,0.0001422023,0.00009920882,0.001245872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003596366,"threshold_uncertainty_score":0.007150888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01983426997428753,"score_gpt":0.2138296212027036,"score_spread":0.1939953512284161,"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."}}