{"id":"W2000207432","doi":"10.1364/ao.50.000891","title":"Infrared species tomography of a transient flow field using Kalman filtering","year":2011,"lang":"en","type":"article","venue":"Applied Optics","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Kalman filter; Smoothness; Optics; Tomography; Noise (video); Computer science; Fast Kalman filter; Extended Kalman filter; Physics; Algorithm; Mathematics; Computer vision; Artificial intelligence; Mathematical analysis","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005306938,0.0001350401,0.0001456154,0.000008323285,0.0000581853,0.000005804453,0.0001847131,0.00006926491,0.001074413],"category_scores_gemma":[0.00000212964,0.0001333589,0.0000670893,0.0001532005,0.000194053,0.00006476358,0.000132365,0.00008611711,0.00001887654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004370425,"about_ca_system_score_gemma":0.000002482859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000523365,"about_ca_topic_score_gemma":0.000006022333,"domain_scores_codex":[0.999192,0.000005769166,0.0002089071,0.0001900564,0.0001842198,0.0002190365],"domain_scores_gemma":[0.9996479,0.00001456861,0.00006800039,0.0001975588,0.000001529485,0.00007051227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003988721,0.001213917,0.1128189,0.0001359857,0.0002296528,0.00005378636,0.02342586,0.44001,0.3725725,0.008041973,0.0004352471,0.04066331],"study_design_scores_gemma":[0.003025142,0.001165374,0.4502808,0.0001162379,0.0004424182,0.00005072467,0.007999492,0.27198,0.2381693,0.01629814,0.007618862,0.002853489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7453763,0.000008858905,0.1268361,0.000004757428,0.00005516775,0.0001412668,0.000002367964,0.00002521619,0.1275499],"genre_scores_gemma":[0.7388581,0.0000208351,0.2607609,0.00009198741,0.00001162007,0.000004785684,0.000001251025,0.00001392708,0.0002366432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3374619,"threshold_uncertainty_score":0.9998388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01609243187815125,"score_gpt":0.1839250385536453,"score_spread":0.167832606675494,"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."}}