{"id":"W4386548534","doi":"10.3997/2214-4609.2023628018","title":"Improving the 4D Signal Inside the SOA at Eldfisk Using RTM Pseudo-Offset Gathers","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"ConocoPhillips (Canada)","funders":"","keywords":"Offset (computer science); Computer science; Signal processing; Computer hardware; Digital signal processing","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":[0.0004434724,0.0006029331,0.0003599976,0.0007213442,0.0003312436,0.0008847399,0.0004533471,0.0004791084,0.002829458],"category_scores_gemma":[0.0009436979,0.000283621,0.0005003318,0.0005297688,0.0002963572,0.0007080585,0.000664834,0.0005774169,0.0007815619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003846729,"about_ca_system_score_gemma":0.0005623826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006175462,"about_ca_topic_score_gemma":0.02045345,"domain_scores_codex":[0.9997826,0.00002089814,0.000009088579,0.00003970107,0.0001048642,0.00004284137],"domain_scores_gemma":[0.9996305,0.00005873397,0.00003200762,0.00008351904,0.0001531902,0.00004202069],"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":[0.001914836,0.0003332044,0.05349202,0.0004334848,0.0002121725,0.001147528,0.001604051,0.1719893,0.5094439,0.001429164,0.006811543,0.2511888],"study_design_scores_gemma":[0.000166708,0.0006103097,0.1883657,0.00009188096,0.0001675535,0.0006387213,0.001097554,0.614279,0.171562,0.001225202,0.02152611,0.0002693787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9340651,0.0001604953,0.05197616,0.0004147424,0.0001822973,0.00004690015,0.002329084,0.003663828,0.007161397],"genre_scores_gemma":[0.920931,0.00008826286,0.07382717,0.00005118454,0.00004905456,0.00003068653,0.002493368,0.0003464622,0.002182754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006175462,"threshold_uncertainty_score":0.01227903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03598054437507704,"score_gpt":0.257930669332602,"score_spread":0.221950124957525,"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."}}