{"id":"W4317496151","doi":"10.1109/lgrs.2023.3238297","title":"Complex Magnetic Anomaly Detection Using Structured Low-Rank Approximation With Total Variation Regularization","year":2023,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Total variation denoising; Regularization (linguistics); Computer science; Signal-to-noise ratio (imaging); Anomaly detection; Noise reduction; Algorithm; Distortion (music); Artificial intelligence; Telecommunications","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":[0.0001805697,0.0001564254,0.0001212059,0.0002665873,0.0002355256,0.0001149867,0.00005410648,0.00006470249,7.06612e-7],"category_scores_gemma":[0.00002680997,0.0001510848,0.00001764453,0.0008370487,0.0001420166,0.0002820454,0.00001416294,0.0001129297,0.000001599563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009502304,"about_ca_system_score_gemma":0.00001173877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001778975,"about_ca_topic_score_gemma":0.00001982376,"domain_scores_codex":[0.9990411,0.00003789376,0.0001626956,0.0002750919,0.0002231139,0.0002600865],"domain_scores_gemma":[0.999651,0.00003008959,0.00006275524,0.0001624853,0.00005076202,0.00004296968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005679178,7.82115e-7,0.00003565245,0.00003908901,0.000003550903,0.000006811299,0.0002406063,0.006934533,0.9721361,0.000008668936,0.000004317333,0.02058417],"study_design_scores_gemma":[0.0001504323,0.00004206176,0.04743668,0.00008602517,0.00001705677,0.0001858473,0.00002393448,0.9280113,0.02201188,0.001829224,0.000001087248,0.0002044772],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5506195,0.000001923955,0.4485009,0.00004303718,0.0001679485,0.0001238605,9.942075e-7,0.0005163956,0.00002539894],"genre_scores_gemma":[0.6594135,0.000001760119,0.3404547,0.00004495281,0.00005658952,1.196685e-7,0.000005689348,0.00002014295,0.000002655145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9501243,"threshold_uncertainty_score":0.616106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01272780915247731,"score_gpt":0.2085885409822321,"score_spread":0.1958607318297548,"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."}}