{"id":"W2022504243","doi":"10.1007/bf02345295","title":"Identification of physiological systems: Estimation of linear timevarying dynamics with non-white inputs and noisy outputs","year":2001,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Control Systems and Identification","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Moore–Penrose pseudoinverse; Mathematics; Autocorrelation; Least-squares function approximation; Autocorrelation matrix; Sampling (signal processing); Algorithm; Inverse; Impulse response; Control theory (sociology); Matrix (chemical analysis); Linear least squares; Non-linear least squares; Statistics; Estimation theory; Singular value decomposition; Computer science; Mathematical analysis; Artificial intelligence","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":[0.000771154,0.0005462244,0.0005163249,0.0002920386,0.0002381311,0.0006360178,0.000407637,0.000924069,0.0004479068],"category_scores_gemma":[0.004165892,0.0003916656,0.0003175061,0.0002669997,0.000669995,0.0009505103,0.0005794825,0.0005290625,0.0002466102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002068467,"about_ca_system_score_gemma":0.0005047666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001034771,"about_ca_topic_score_gemma":0.0008034441,"domain_scores_codex":[0.9995815,0.0001361612,0.00002858527,0.0001126166,0.0001145004,0.00002664639],"domain_scores_gemma":[0.9991733,0.0005398142,0.0001261573,0.00007246291,0.00007306078,0.00001524399],"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":[0.0005067536,0.0001856572,0.002690783,0.0008925913,0.0001393947,0.000324048,0.0004533802,0.58561,0.1053575,0.0184658,0.001068829,0.2843053],"study_design_scores_gemma":[0.00001246596,0.00007398078,0.002196573,0.00002440318,0.00002067178,0.000105777,0.00002676602,0.9725631,0.01831055,0.005733229,0.0009157987,0.00001668569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01986232,0.000354873,0.9790788,0.00009575153,0.00003177651,0.00002073033,0.00001705035,0.0001606858,0.0003780746],"genre_scores_gemma":[0.796706,0.001560234,0.1970869,0.000097009,0.0001113706,0.0001838823,0.0001532424,0.00008492585,0.004016245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001034771,"threshold_uncertainty_score":0.004078269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007445477815462453,"score_gpt":0.2105881477172927,"score_spread":0.2031426699018303,"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."}}