{"id":"W4389500539","doi":"10.21203/rs.3.rs-3715365/v1","title":"Efficient proxy for Time-Lapse Seismic ForwardModeling using U-Net Encoder-Decoder Approach","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universidade Estadual de Campinas; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Energi Simulation; Shell Brasil; U.S. Department of Energy","keywords":"Computer science; Proxy (statistics); Benchmark (surveying); Data mining; Inference; Feature selection; Artificial intelligence; Machine learning; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008062638,0.000794975,0.0008059534,0.0004322096,0.0003264348,0.0007682102,0.001243772,0.00102299,0.002137878],"category_scores_gemma":[0.002103923,0.0004063594,0.0004875271,0.0003915536,0.000516149,0.000935123,0.0009784092,0.001131618,0.0005595301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000817269,"about_ca_system_score_gemma":0.001257968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008426704,"about_ca_topic_score_gemma":0.006793,"domain_scores_codex":[0.9997836,0.0000696092,0.00001388207,0.00005091893,0.00004964698,0.00003238963],"domain_scores_gemma":[0.9993394,0.0003433131,0.00006692452,0.0000708885,0.0001356465,0.00004376862],"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":[0.00008212545,0.00003142139,0.0007213578,0.00003494812,0.00002090853,0.00005918107,0.00002863087,0.9450114,0.002285493,0.005092902,0.000457652,0.04617398],"study_design_scores_gemma":[8.394094e-7,0.000003448856,0.00001199762,8.524526e-7,9.007077e-7,0.000001830828,9.0298e-7,0.9992155,0.0004123214,0.000293851,0.00005679322,7.851978e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02145533,0.0001238346,0.9762213,0.00009909467,0.00002749268,0.00002351609,0.00007046306,0.001057655,0.0009213374],"genre_scores_gemma":[0.6949744,0.0001509259,0.3000583,0.0001249224,0.00004059018,0.000152357,0.0004631655,0.000215206,0.003820119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008426704,"threshold_uncertainty_score":0.01675528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1346833565702825,"score_gpt":0.4054106020847569,"score_spread":0.2707272455144745,"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."}}