{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004800049,0.0001534901,0.000179455,0.0001100154,0.0003091276,0.00005932408,0.0005231612,0.00008011165,0.00005672496],"category_scores_gemma":[0.0001314019,0.0001096506,0.00004238041,0.0003856194,0.0001338219,0.0003765854,0.00005096992,0.0002475199,0.00007077858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001710556,"about_ca_system_score_gemma":0.00008189566,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04393752,"about_ca_topic_score_gemma":0.0006287848,"domain_scores_codex":[0.998835,0.0001280113,0.0002805509,0.0003192532,0.0002336776,0.0002035244],"domain_scores_gemma":[0.9989263,0.0001594566,0.0001525796,0.0006649848,0.00006800993,0.00002869454],"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":[0.0004583873,0.0001588156,0.003985193,0.0002488791,0.0001546618,0.000005948397,0.003604819,0.02488678,0.01067253,0.005823277,0.1705285,0.7794722],"study_design_scores_gemma":[0.0001935462,0.0001207065,0.02050495,0.0003024381,0.00004241381,0.000001034938,0.0004348456,0.9275726,0.02565113,0.01879374,0.006198376,0.0001842231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9447023,0.0001691545,0.04964292,0.002550279,0.0006901163,0.0002606071,0.0001538027,0.0001542842,0.001676496],"genre_scores_gemma":[0.9955479,0.00007340358,0.001664268,0.001023109,0.0001303053,0.000001807717,0.001080598,0.000004151347,0.000474487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9026858,"threshold_uncertainty_score":0.962429,"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."}}