{"id":"W2319063701","doi":"10.1190/1.3513105","title":"Complex spectral decomposition via inversion strategies","year":2010,"lang":"en","type":"article","venue":"","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Algorithm; Computer science; Time–frequency analysis; Matrix decomposition; Inverse problem; Inversion (geology); Fourier transform; Wavelet; Norm (philosophy); Signal processing; Time–frequency representation; Radio spectrum; Acoustics; Mathematics; Geology; Artificial intelligence; Telecommunications; Physics; Seismology; Mathematical analysis","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009851021,0.00007376059,0.00006598402,0.00006444904,0.0001305493,0.00007538181,0.0001294378,0.00004408216,0.01355665],"category_scores_gemma":[0.000003154453,0.00005851229,0.00003373108,0.00007978926,0.0000799787,0.0003392176,0.000005500309,0.0001520893,0.0005862827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001461251,"about_ca_system_score_gemma":0.00001854698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005848069,"about_ca_topic_score_gemma":0.0004578207,"domain_scores_codex":[0.9994891,0.00001719103,0.0000883631,0.0001332173,0.0001166146,0.0001555532],"domain_scores_gemma":[0.9997485,0.00002545815,0.00002510352,0.0001124979,0.0000189935,0.00006942929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000787493,0.00006465211,0.1667084,0.00002585355,0.00002204728,0.00002850313,0.000332353,0.0001836134,0.1300695,0.005509403,0.2869627,0.4100142],"study_design_scores_gemma":[0.000420221,0.0002753981,0.3088468,0.00001012827,0.00001452629,0.0001050585,0.0007541676,0.4753483,0.06456979,0.03663215,0.1125145,0.0005090119],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8043332,0.000008391292,0.006783008,0.001109803,0.0004068288,0.00007088254,0.000006708817,0.0003473002,0.1869339],"genre_scores_gemma":[0.9818479,0.000005239594,0.01603515,0.001740896,0.00008015113,1.396645e-7,0.0000993079,0.000001483138,0.0001897717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4751647,"threshold_uncertainty_score":0.9873451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0144934018583117,"score_gpt":0.2417564711215844,"score_spread":0.2272630692632726,"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."}}