{"id":"W4378231287","doi":"10.3997/2214-4609.202310841","title":"Velocity model building with deep learning: application to subsea permafros characterization","year":2023,"lang":"en","type":"article","venue":"","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; Polytechnique Montréal","funders":"","keywords":"Permafrost; Subsea; Classification of discontinuities; Geology; Context (archaeology); Seismology; Remote sensing; Geotechnical engineering; Oceanography; Paleontology","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":[0.0005216721,0.0008725958,0.0004488985,0.0007975955,0.0003790213,0.0006802295,0.0007905237,0.0009035462,0.001504673],"category_scores_gemma":[0.001511751,0.0003333795,0.0005634981,0.0007970834,0.0002752502,0.0005562543,0.0005825373,0.0008715795,0.0003938584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008762318,"about_ca_system_score_gemma":0.0009020469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05348247,"about_ca_topic_score_gemma":0.0505158,"domain_scores_codex":[0.9998516,0.00002426194,0.000008986639,0.0000594733,0.00002607457,0.00002964843],"domain_scores_gemma":[0.9995571,0.0001948553,0.00003432054,0.00004922598,0.0001326408,0.00003183037],"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":[0.00005559902,0.00005102477,0.003809124,0.00002249577,0.0000422424,0.00006581569,0.00003034417,0.8961022,0.00237272,0.0003417957,0.001239305,0.09586743],"study_design_scores_gemma":[0.00000165441,0.000003692791,0.0002258304,0.000001510558,0.000001604984,0.000003124118,0.00000602687,0.9988151,0.0005145662,0.0002984237,0.000126502,0.000001995347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5419978,0.0007189941,0.4397868,0.0007965718,0.0001601865,0.00008371264,0.001835336,0.00999815,0.004622382],"genre_scores_gemma":[0.9080054,0.0001096615,0.08763377,0.0001044715,0.00002892615,0.00004165675,0.001690874,0.0001832934,0.002201954],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.05348247,"threshold_uncertainty_score":0.1063423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0122123009253691,"score_gpt":0.2192430448574147,"score_spread":0.2070307439320457,"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."}}