{"id":"W3004714625","doi":"10.1190/tle39020102.1","title":"Facies — The drivers for modern inversions","year":2020,"lang":"en","type":"article","venue":"The Leading Edge","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Society of Petroleum Geologists; Virtual Materials Group (Canada)","funders":"","keywords":"Facies; Geology; Inversion (geology); Bayesian inference; Bayesian probability; Bayes' theorem; Workflow; Seismic inversion; Inference; Reservoir modeling; Algorithm; Data mining; Computer science; Mathematics; Seismology; Artificial intelligence; Geometry; Geotechnical engineering; Paleontology; Azimuth","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":[0.000168629,0.000066159,0.0000683679,0.00001500791,0.0004267062,0.00004600156,0.0003663878,0.00002292812,0.0001467761],"category_scores_gemma":[0.00006943484,0.00003383486,0.0000585099,0.00009968488,0.0001430607,0.000089088,0.00001534686,0.0001155712,0.0003811105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002482068,"about_ca_system_score_gemma":0.00001663466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001776629,"about_ca_topic_score_gemma":0.000007805825,"domain_scores_codex":[0.9995331,0.00003432708,0.00006560466,0.0001132281,0.00009328571,0.0001605],"domain_scores_gemma":[0.9995583,0.0002239995,0.0000287882,0.0001257916,0.00001525298,0.00004790783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002104955,0.000002179051,0.005377939,0.00001069123,0.00001613227,8.176837e-7,0.006871523,0.0006318094,0.000216275,0.0003143552,0.9116941,0.07484311],"study_design_scores_gemma":[0.0001244569,0.00006115487,0.0006270595,0.000008747949,0.00001986642,0.00000258281,0.001242055,0.2583374,0.001941744,0.003420136,0.7341126,0.0001021326],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3354539,0.001592714,0.06698117,0.4911857,0.00201434,0.001749143,0.0002997873,0.001539852,0.09918343],"genre_scores_gemma":[0.9772258,0.00003477897,0.0004387665,0.02075528,0.0001354624,0.000001493308,0.00001412914,0.000002711815,0.001391616],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6417719,"threshold_uncertainty_score":0.4898531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05153383622886609,"score_gpt":0.2356335088454768,"score_spread":0.1840996726166107,"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."}}