{"id":"W4408362490","doi":"10.1190/geo2024-0557.1","title":"CycleGAN integration of high-resolution crooked lines into 3D seismic volumes: Enhancing data set resolution on the Loess Plateau, China","year":2025,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Loess plateau; Geology; High resolution; Resolution (logic); China; Loess; Plateau (mathematics); Seismology; Remote sensing; Geomorphology; Computer science; Geography; Soil science; Archaeology; Artificial intelligence; Mathematics","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":[0.000685872,0.0007825512,0.0005122833,0.0005759029,0.0001699602,0.0004950853,0.0009235044,0.0005116323,0.001003413],"category_scores_gemma":[0.001619876,0.0002642099,0.0004988308,0.0005452262,0.0005081827,0.0006033228,0.001055914,0.0006660869,0.0002879013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003583565,"about_ca_system_score_gemma":0.0004967336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005610161,"about_ca_topic_score_gemma":0.007354563,"domain_scores_codex":[0.9996706,0.00007115342,0.00001451702,0.0001045251,0.00009153452,0.00004762314],"domain_scores_gemma":[0.9995783,0.0001238537,0.00005089595,0.0001005575,0.0001079524,0.00003850925],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002742151,0.00009723481,0.007613366,0.00008415405,0.00008287307,0.0002760725,0.0001078354,0.7987601,0.01362064,0.001186972,0.002219058,0.1756774],"study_design_scores_gemma":[0.000003961958,0.00002557553,0.001084066,0.000004178811,0.000005684342,0.00002199463,0.00001522958,0.9958069,0.002179219,0.0004630192,0.0003840076,0.000006130165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.457768,0.0005097142,0.5352635,0.000510278,0.000133108,0.00008542089,0.000835072,0.002293548,0.002601282],"genre_scores_gemma":[0.9450449,0.0001700738,0.05089679,0.0001761439,0.00003388626,0.00004675091,0.001826362,0.0001220974,0.001683007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005610161,"threshold_uncertainty_score":0.01115501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01806805525567791,"score_gpt":0.2444899803127781,"score_spread":0.2264219250571002,"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."}}