{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003461877,0.0007340659,0.0004370373,0.002002824,0.0007378479,0.002106783,0.001041227,0.0007454953,0.00460892],"category_scores_gemma":[0.01866521,0.0007111212,0.0006927553,0.001602689,0.001200474,0.003282197,0.001732712,0.001862547,0.0009183339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00139172,"about_ca_system_score_gemma":0.001049958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02253109,"about_ca_topic_score_gemma":0.01853592,"domain_scores_codex":[0.998687,0.0002771488,0.00007277737,0.0003546284,0.000470846,0.000137702],"domain_scores_gemma":[0.9929616,0.003006726,0.0008192761,0.001353972,0.001649714,0.000208695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003421449,0.0001337539,0.2131242,0.000262217,0.0002186191,0.0005438924,0.001064837,0.2964357,0.02060167,0.0568315,0.006320463,0.404121],"study_design_scores_gemma":[0.00002782652,0.00006115132,0.1000181,0.0001904399,0.00006620369,0.0003368614,0.0005628259,0.7820688,0.009287291,0.08226939,0.02495189,0.000159178],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4044466,0.001001184,0.5669689,0.004080402,0.0001215288,0.00009788637,0.002011647,0.00412348,0.01714847],"genre_scores_gemma":[0.9370547,0.0002889479,0.05977643,0.0001322654,0.00007923562,0.00002606761,0.000869161,0.0005329843,0.001240361],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02253109,"threshold_uncertainty_score":0.04479986,"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."}}