{"id":"W2925183314","doi":"10.1190/geo2018-0134.1","title":"Gravity modeling for crustal-scale models of rifted continental margins using a constrained 3D inversion method","year":2019,"lang":"en","type":"article","venue":"Geophysics","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"National Natural Science Foundation of China","keywords":"Inversion (geology); Geology; Weighting; A priori and a posteriori; Bathymetry; Covariance; Seismic inversion; Probabilistic logic; Synthetic data; Geodesy; Inverse problem; Seismology; Algorithm; Computer science; Tectonics; Geometry; Mathematics; Mathematical analysis","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":[0.0002272077,0.0004019017,0.0002822191,0.0004132253,0.0002526705,0.0005459061,0.0005628692,0.0005536157,0.00104754],"category_scores_gemma":[0.0009551739,0.000296828,0.0005139724,0.0003081632,0.0003674175,0.000377186,0.0005078224,0.0004339479,0.0001445707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004158998,"about_ca_system_score_gemma":0.0007806427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02441783,"about_ca_topic_score_gemma":0.0141308,"domain_scores_codex":[0.9999394,0.000021582,0.000003826529,0.00001053812,0.00001779176,0.000006812191],"domain_scores_gemma":[0.9998083,0.00009994105,0.00002757568,0.00001553834,0.00003736672,0.00001134431],"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":[0.000003676611,0.000003832401,0.0002404001,0.00000418582,0.000005559544,0.00001984419,0.00001165538,0.9944008,0.0007259742,0.001676171,0.00005346717,0.002854418],"study_design_scores_gemma":[0.000001058395,7.926848e-7,0.00004264387,5.649513e-7,5.068085e-7,0.000001795722,0.000001262834,0.9994889,0.0000380906,0.0003754857,0.00004778024,0.000001118447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08417971,0.00008355123,0.9130306,0.000155351,0.00001020151,0.00002505641,0.0001430734,0.0003407839,0.002031635],"genre_scores_gemma":[0.8573003,0.0001390526,0.1409715,0.00005343197,0.00001678895,0.00008082581,0.0002128512,0.0001186302,0.001106672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02441783,"threshold_uncertainty_score":0.04855138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02788985564108396,"score_gpt":0.2620512093359676,"score_spread":0.2341613536948837,"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."}}