{"id":"W3012889279","doi":"10.1007/s13202-020-00843-2","title":"Data-driven model for shear wave transit time prediction for formation evaluation","year":2020,"lang":"en","type":"article","venue":"Journal of Petroleum Exploration and Production Technology","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Hibernia Management and Development Company; Research and Development Corporation of Newfoundland and Labrador; Canada Research Chairs","keywords":"Shear (geology); Well logging; Geology; Monte Carlo method; Transit time; Train; Shear modulus; Geotechnical engineering; Petroleum engineering; Engineering; Petrology; Statistics; Mathematics; Materials science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007732915,0.0001008283,0.000193155,0.000271324,0.00007115877,0.00002751255,0.00009300893,0.0001256527,0.000004262752],"category_scores_gemma":[0.0004298013,0.00009987214,0.00004169197,0.0001725838,0.00001720727,0.001205295,0.000009338411,0.0001411008,0.000001126214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000496956,"about_ca_system_score_gemma":0.00002808756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.805099e-8,"about_ca_topic_score_gemma":4.279859e-7,"domain_scores_codex":[0.99909,0.00002812683,0.0004605149,0.0001485798,0.0001696176,0.0001032199],"domain_scores_gemma":[0.9992528,0.00002989622,0.0001401775,0.0001510972,0.000376417,0.00004961249],"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":[0.00007997415,0.000008093151,0.000002279371,0.00009726699,0.00002559416,6.793109e-8,0.0003127389,0.9594999,0.01803145,0.00007741285,0.002318834,0.0195464],"study_design_scores_gemma":[0.0009256305,0.0002317945,0.000004480233,0.00001547752,0.00007973787,0.00001599545,0.0001144216,0.9849935,0.00662148,0.002496086,0.004420199,0.00008125367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0193766,0.0002702217,0.9706243,0.00880765,0.0003087576,0.0003771938,0.00005530745,0.0001716646,0.000008342819],"genre_scores_gemma":[0.8154163,0.0003915184,0.1831481,0.00003118194,0.0006477359,0.00008376023,0.0002074867,0.00003381768,0.00004004834],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7960398,"threshold_uncertainty_score":0.4072667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1167763543832884,"score_gpt":0.3030910056383141,"score_spread":0.1863146512550257,"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."}}