{"id":"W3045962146","doi":"10.1190/geo2019-0614.1","title":"Geophysical inversion for 3D contact surface geometry","year":2020,"lang":"en","type":"article","venue":"Geophysics","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Allison University; Memorial University of Newfoundland","funders":"","keywords":"Inversion (geology); Geology; Synthetic data; Inverse problem; Algorithm; Geophysics; Earth structure; Environmental geology; Computer science; Inverse method; Polygon mesh; Geometry; Mathematics; Tectonics; Applied mathematics; Seismology; Computer graphics (images)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006779451,0.0001272355,0.00022869,0.00001403998,0.0001415788,0.00003578151,0.0001813865,0.00006220515,0.0003768587],"category_scores_gemma":[0.00005771974,0.0001009204,0.0001599557,0.0002990591,0.00002636466,0.0001081045,0.00001595263,0.0001250324,0.001154477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001785285,"about_ca_system_score_gemma":0.00001970941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006933769,"about_ca_topic_score_gemma":0.00003026225,"domain_scores_codex":[0.9990661,0.00002752962,0.0001377162,0.0003030759,0.0001839255,0.0002816889],"domain_scores_gemma":[0.9994453,0.0001408376,0.00005248925,0.0001194848,0.00005206711,0.0001898134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005811485,0.0002097343,0.3503026,0.0002503391,0.0003352021,0.00003380626,0.0008962539,0.3100792,0.001949493,0.001453329,0.01672988,0.3171791],"study_design_scores_gemma":[0.0003930088,0.0004190099,0.04500071,0.000006099014,0.00006534175,3.309077e-7,0.00008794785,0.9397311,0.0001058701,0.002348675,0.01158752,0.0002543906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9864715,0.00009608333,0.010413,0.001720281,0.0001250224,0.0001139865,0.0001265683,0.00008283187,0.0008507777],"genre_scores_gemma":[0.9936104,0.00001540736,0.003822563,0.001781543,0.0003818961,4.59138e-7,0.0002554759,0.000003185673,0.0001290876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6296519,"threshold_uncertainty_score":0.9996232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02706209333468759,"score_gpt":0.2094776018569622,"score_spread":0.1824155085222746,"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."}}