{"id":"W2991074847","doi":"10.1051/e3sconf/201913301008","title":"VSP data inversion for vertical velocity gradient and elliptical anisotropy model","year":2019,"lang":"en","type":"article","venue":"E3S Web of Conferences","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Husky Energy (Canada)","funders":"","keywords":"Maxima and minima; Anisotropy; Inversion (geology); Borehole; Geology; Velocity gradient; Normal moveout; Geometry; Geodesy; Mathematical analysis; Algorithm; Mathematics; Physics; Mechanics; Optics; Seismology","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":[0.0006448766,0.0005751328,0.0004304758,0.0004981651,0.0002437111,0.0005944193,0.0004830891,0.0006221869,0.001254597],"category_scores_gemma":[0.002609395,0.0002858549,0.0004664689,0.0006154909,0.0002709032,0.0007793326,0.0005204962,0.0007365166,0.0003056749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003397459,"about_ca_system_score_gemma":0.0008969137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01462123,"about_ca_topic_score_gemma":0.01106052,"domain_scores_codex":[0.9997759,0.00006456749,0.00001168425,0.00005191262,0.00005937716,0.00003649967],"domain_scores_gemma":[0.9994411,0.0002764674,0.00004562079,0.00008068079,0.0001290799,0.00002711361],"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":[0.0001830941,0.00006043656,0.006099847,0.0001058112,0.0000476057,0.00009958833,0.0001007927,0.9225397,0.02098131,0.003065388,0.0008510974,0.04586525],"study_design_scores_gemma":[0.00000807687,0.00001293249,0.001127173,0.000003697011,0.000005391955,0.00001368667,0.00002543832,0.994269,0.003457807,0.0007752362,0.0002943515,0.000007265216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5293291,0.0001391842,0.461933,0.0003098683,0.00003732153,0.00006028794,0.001890054,0.001673413,0.004627759],"genre_scores_gemma":[0.9229624,0.00004523065,0.0749426,0.00001615421,0.000005864084,0.00003348852,0.000961347,0.0001569292,0.000875868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01462123,"threshold_uncertainty_score":0.02907223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04676113008372014,"score_gpt":0.2534107762986967,"score_spread":0.2066496462149766,"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."}}