{"id":"W2586516515","doi":"10.1111/1365-2478.12484","title":"Using constrained inversion of gravity and magnetic field to produce a 3D litho‐prediction model","year":2017,"lang":"en","type":"article","venue":"Geophysical Prospecting","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"RPM International (Canada); Laurentian University","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Geology; Inversion (geology); Diorite; Hydrogeology; Geophysics; Potential field; Gravity anomaly; Mineralogy; Quartz; Geodesy; Geomorphology; Oil field; Structural basin; Geotechnical engineering; Paleontology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002255698,0.0001275486,0.0002412704,0.00003886545,0.0004595063,0.00007267734,0.0001637192,0.00005881806,0.0000289109],"category_scores_gemma":[0.0006612382,0.0001062276,0.00005187301,0.0001380884,0.0001126338,0.0001906789,0.00005933945,0.0002036306,0.00001291197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003910048,"about_ca_system_score_gemma":0.00002583421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002733786,"about_ca_topic_score_gemma":0.00006243822,"domain_scores_codex":[0.9989198,0.00004386633,0.00018738,0.0003618174,0.0002065483,0.0002805843],"domain_scores_gemma":[0.9992442,0.0001496899,0.0001183647,0.0002614786,0.00006399585,0.0001622739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001816777,0.0001008903,0.1236029,0.0001178036,0.00001591217,0.000006302532,0.0004227771,0.003140209,0.04203015,0.0006125921,0.00002471126,0.829744],"study_design_scores_gemma":[0.0002287352,0.0006779501,0.3867235,0.00004195769,0.00002661978,0.000003675941,0.00002050679,0.578824,0.005543985,0.02775124,0.000007404599,0.000150329],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945415,0.00002545945,0.003219425,0.0003972017,0.0001158831,0.0002482041,0.0000186742,0.00002982965,0.001403794],"genre_scores_gemma":[0.9735211,0.000001573381,0.02610957,0.00008640073,0.0001833434,0.000001047293,0.000002201183,0.00000294554,0.00009181017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8295937,"threshold_uncertainty_score":0.4331836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03089049668367077,"score_gpt":0.2718241763494931,"score_spread":0.2409336796658224,"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."}}