{"id":"W1524460150","doi":"10.1111/j.1365-2478.2011.01052.x","title":"Large‐scale 3D inversion of potential field data","year":2012,"lang":"en","type":"article","venue":"Geophysical Prospecting","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Inversion (geology); Scaling; Regularization (linguistics); Potential field; Inverse problem; Geology; Linear scale; Synthetic data; Geophysics; Gravitational field; Computer science; Algorithm; Geodesy; Mathematics; Physics; Seismology; Geometry; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000263738,0.000388235,0.0003029959,0.0007256711,0.0003449594,0.0006748945,0.0005450163,0.0003555244,0.001211793],"category_scores_gemma":[0.00115475,0.000271854,0.0003131474,0.0007420615,0.000467216,0.0003375608,0.0005603419,0.0003874828,0.0002069341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00101917,"about_ca_system_score_gemma":0.001816861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0883785,"about_ca_topic_score_gemma":0.1172495,"domain_scores_codex":[0.9998913,0.0000268188,0.000003966562,0.00001695747,0.00004476893,0.0000162319],"domain_scores_gemma":[0.9996476,0.0001697612,0.00003258452,0.00003844967,0.00009033473,0.00002132614],"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":[0.00007732881,0.00007728133,0.006008448,0.00006110589,0.0000590944,0.000246183,0.0001893612,0.9091893,0.02770986,0.002415537,0.001544641,0.05242189],"study_design_scores_gemma":[0.000007939647,0.00000394213,0.002160875,0.000002020313,0.000001994977,0.00001164576,0.00002375147,0.9953998,0.001588494,0.000428652,0.000363958,0.000006988375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.654687,0.0001255163,0.3341764,0.0005419076,0.00003410447,0.00008570054,0.0009569554,0.003078137,0.006314237],"genre_scores_gemma":[0.9245988,0.00003270066,0.07405273,0.00003983306,0.000008206865,0.00002738075,0.0005565659,0.0001172667,0.0005666111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0883785,"threshold_uncertainty_score":0.1757281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02088521537879673,"score_gpt":0.2560086261450942,"score_spread":0.2351234107662975,"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."}}