{"id":"W4385695886","doi":"10.1109/tgrs.2023.3303449","title":"Parametric Convolutional Dictionary Learning and its Applications to Seismic Data Processing","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; China Scholarship Council","keywords":"Computer science; Convolution (computer science); Deconvolution; Pattern recognition (psychology); Artificial intelligence; Filter (signal processing); Convolutional neural network; Waveform; Algorithm; K-SVD; Parametric statistics; Superposition principle; Wavelet; Signal reconstruction; Basis function; Signal processing; Sparse approximation; Mathematics; Computer vision; Digital signal processing; Artificial neural network","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":[],"consensus_categories":[],"category_scores_codex":[0.0003728009,0.0001062247,0.00009729687,0.000434788,0.001108867,0.00009832866,0.0001254362,0.00004829004,0.0000148251],"category_scores_gemma":[0.00002271127,0.00009682366,0.00001589086,0.001315711,0.0001350577,0.0003371062,0.000004347943,0.000211811,0.00008977824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006749064,"about_ca_system_score_gemma":0.00005716135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007415472,"about_ca_topic_score_gemma":0.00001729429,"domain_scores_codex":[0.9988788,0.00003968287,0.0001383619,0.0004564302,0.0002361972,0.0002505524],"domain_scores_gemma":[0.9994785,0.0001605318,0.00003829316,0.0001369994,0.00004553227,0.0001401882],"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":[0.00000568553,0.000003074089,0.00003306064,0.00001147078,0.000002373555,0.000002656019,0.00007450921,0.002866633,0.0001396719,7.519213e-7,0.0001234079,0.9967367],"study_design_scores_gemma":[0.00006467063,0.00004685957,0.001363383,0.00005183294,0.00001095496,0.00008676598,0.0002921919,0.9830297,0.0004326125,0.0001542972,0.0143373,0.0001294628],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08761161,0.0002464125,0.9093292,0.001590593,0.0002102776,0.0002267066,0.00005678417,0.0003796332,0.0003487373],"genre_scores_gemma":[0.9827142,0.0006024081,0.01506546,0.0007401864,0.00004066617,1.744856e-7,0.00002908143,0.000004516417,0.0008033441],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9966072,"threshold_uncertainty_score":0.8528619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03579242087980563,"score_gpt":0.2727583840942421,"score_spread":0.2369659632144365,"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."}}