{"id":"W2110817419","doi":"10.1109/icassp.1997.604773","title":"Estimation of transfer function parameters with output Fourier transform sensitivity vectors","year":2002,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Transfer function; Parametric statistics; Context (archaeology); Fourier transform; Mathematics; Nonlinear system; Covariance matrix; Estimation theory; Parameter space; Boundary (topology); Mathematical analysis; Function (biology); Algorithm; Computer science; Physics; Statistics","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":[0.0008111889,0.0009275224,0.0005062015,0.00058475,0.0002532034,0.0005906688,0.0004190493,0.000815067,0.001245177],"category_scores_gemma":[0.005168557,0.000506256,0.0005993279,0.0003470906,0.0006681707,0.001131713,0.0006987003,0.0009193823,0.0004638274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003511981,"about_ca_system_score_gemma":0.0005291712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001104819,"about_ca_topic_score_gemma":0.001002548,"domain_scores_codex":[0.9996473,0.00009589503,0.00001869136,0.0000633338,0.0001470669,0.00002769848],"domain_scores_gemma":[0.9988909,0.0007021,0.0001347513,0.0001450477,0.0001129034,0.00001423416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001243416,0.00009062775,0.001386244,0.0001470884,0.00007807811,0.0001708867,0.0001763613,0.6712486,0.06753036,0.03807196,0.0006378372,0.2203376],"study_design_scores_gemma":[0.000008919123,0.00004523409,0.0006373526,0.00001607262,0.0000151401,0.000109075,0.00001354428,0.9625841,0.02264386,0.01309466,0.0008072301,0.00002475069],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00615545,0.00002310668,0.99335,0.00003556836,0.000004731568,0.0000109395,0.00001844973,0.0001078201,0.0002940429],"genre_scores_gemma":[0.3793122,0.0002715697,0.6177036,0.00007271914,0.00004475617,0.0001425074,0.0002006473,0.00009415098,0.002157957],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001245177,"threshold_uncertainty_score":0.004290044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.017001796075864,"score_gpt":0.1840318981181728,"score_spread":0.1670301020423088,"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."}}