{"id":"W2308116302","doi":"10.1093/gji/ggw097","title":"Source estimation with surface-related multiples—fast ambiguity-resolved seismic imaging","year":2016,"lang":"en","type":"article","venue":"Geophysical Journal International","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Wavelet; Geophysical imaging; Computer science; Algorithm; Inversion (geology); Inverse problem; Compressed sensing; Ambiguity; Multiple; Seismic inversion; Artificial intelligence; Geology; Mathematics; Seismology; Geometry","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000242318,0.0001597935,0.0001483283,0.0001071012,0.0002056658,0.0001641892,0.0003546262,0.00003817892,0.001260654],"category_scores_gemma":[0.00006020789,0.00009564323,0.00009365181,0.0001158172,0.0001612537,0.000679229,0.00002135262,0.0002638487,0.0005707946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003045212,"about_ca_system_score_gemma":0.00005146414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007992836,"about_ca_topic_score_gemma":0.00000652901,"domain_scores_codex":[0.9986082,0.00007063157,0.0002859869,0.0002303614,0.0005248899,0.0002798862],"domain_scores_gemma":[0.9992208,0.0001522481,0.0001978944,0.0001190942,0.000155165,0.0001547469],"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":[0.0001515857,0.0000596569,0.07856394,0.000002783906,0.00009085972,0.00004856836,0.0001995847,0.01221638,0.001546012,0.0000663805,0.008350032,0.8987042],"study_design_scores_gemma":[0.001274436,0.0001135247,0.05126988,0.000199314,0.00002281741,0.000484413,0.0001067861,0.9231395,0.002412,0.004547803,0.01612078,0.0003087439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.796963,0.00004124656,0.1887388,0.009576913,0.0007406555,0.00008946346,0.0000498485,0.0001976124,0.003602411],"genre_scores_gemma":[0.9917775,0.00002190915,0.004977441,0.0009883773,0.0002284301,3.571734e-7,0.00003928945,0.000008044949,0.001958589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9109231,"threshold_uncertainty_score":0.9996523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006802942543217328,"score_gpt":0.208242946406642,"score_spread":0.2014400038634246,"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."}}