{"id":"W2327519634","doi":"10.1190/segam2013-1014.1","title":"Monogenic signal decomposition: A new approach to enhance magnetic data","year":2013,"lang":"en","type":"article","venue":"","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"SIGNAL (programming language); Geology; Magnetic anomaly; Decomposition; Sedimentary rock; Noise (video); Orientation (vector space); Magnetic survey; Computer science; Geophysics; Mineralogy; Remote sensing; Artificial intelligence; Image (mathematics); Paleontology; Mathematics; Chemistry; Geometry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001076415,0.0001028169,0.0001278488,0.00003264354,0.00006523836,0.00008875795,0.0005451959,0.00003545228,0.02578054],"category_scores_gemma":[0.00002380983,0.00007302296,0.00002896053,0.0003767577,0.00001683673,0.0002237937,0.00004208988,0.00009278985,0.01410262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001167649,"about_ca_system_score_gemma":0.00002876965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005737793,"about_ca_topic_score_gemma":0.0001004929,"domain_scores_codex":[0.9989292,0.00006831998,0.0001359059,0.0003988381,0.0001697312,0.0002980227],"domain_scores_gemma":[0.9990737,0.0001348122,0.0000165283,0.0003781081,0.00001997077,0.0003769024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000008347694,0.00002931683,0.0005586296,0.000003257367,0.000004457368,4.934321e-7,0.00001583106,0.0002577807,0.0003321425,0.0000428628,0.02234368,0.9764032],"study_design_scores_gemma":[0.0002706357,0.0009380116,0.6092219,0.00000898855,0.00003815553,0.00002613761,0.00004043027,0.3243235,0.003051667,0.02912915,0.03222012,0.0007313319],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2870225,0.001167468,0.3732811,0.006010906,0.0002944178,0.001355785,0.00005806335,0.0002463177,0.3305635],"genre_scores_gemma":[0.6799781,0.000007107579,0.3049802,0.001936213,0.0003060608,0.000003561966,0.0001397501,0.000002674345,0.01264632],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9756719,"threshold_uncertainty_score":0.986665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03214436101346687,"score_gpt":0.2808837162802618,"score_spread":0.2487393552667949,"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."}}