{"id":"W2157614128","doi":"10.1109/acc.2009.5160458","title":"POD based observer for contaminant flow estimation in building systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Galerkin method; Point of delivery; Vector field; Observer (physics); Nonlinear system; Airflow; Flow (mathematics); Applied mathematics; Mathematics; Computer science; Control theory (sociology); Engineering; Geometry; Physics; Mechanical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007514809,0.00006839659,0.0001005758,0.00003023644,0.00003714254,0.00003188111,0.00003953315,0.00002103974,0.00005507975],"category_scores_gemma":[0.000001362802,0.00005741747,0.00004424476,0.00006140274,0.00000376138,0.00008609016,0.000002622177,0.00004412237,0.000003106297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001331728,"about_ca_system_score_gemma":0.00001435834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004892411,"about_ca_topic_score_gemma":0.000001322975,"domain_scores_codex":[0.9995479,0.00001346974,0.0001421278,0.0001177254,0.00004869432,0.0001300992],"domain_scores_gemma":[0.9998116,0.00002660741,0.00003388587,0.00007560466,0.00002090464,0.00003141174],"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":[0.00005046213,0.0001576967,0.001025,0.0000163345,0.00001090075,5.915724e-7,0.00003367885,0.6973892,0.002175658,0.1108018,0.002305302,0.1860333],"study_design_scores_gemma":[0.000545714,0.00003426814,0.001237464,0.00002925108,0.000004992724,1.678762e-7,0.00002397514,0.9950265,0.001175081,0.00101192,0.0008348939,0.00007574387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1027269,0.00001703091,0.8945515,0.0003384255,0.0002124548,0.0003366858,0.000004584744,0.00002456161,0.001787818],"genre_scores_gemma":[0.9873393,2.170214e-7,0.01195886,0.00007634096,0.0001089021,0.00002838267,0.00002559908,0.000004559473,0.0004578083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8846124,"threshold_uncertainty_score":0.2341416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02038188205033597,"score_gpt":0.2739149921017462,"score_spread":0.2535331100514102,"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."}}