{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001922603,0.0005285438,0.0003033049,0.0005104228,0.0002956635,0.0007765216,0.0009263323,0.0006257948,0.001526922],"category_scores_gemma":[0.0005768701,0.0004950572,0.0005743664,0.0004759713,0.0003762098,0.0003480119,0.000373037,0.0004994929,0.0002536362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001354516,"about_ca_system_score_gemma":0.001891659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1538708,"about_ca_topic_score_gemma":0.09585197,"domain_scores_codex":[0.9999231,0.000008778706,0.00000520534,0.00003025583,0.00001833313,0.00001425849],"domain_scores_gemma":[0.9998306,0.00006175281,0.00001921915,0.00001589602,0.00005441471,0.00001800329],"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":[0.00001344575,0.000009436239,0.001507356,0.000005149997,0.00001250938,0.00002875019,0.00001104329,0.9924653,0.001228496,0.0001809577,0.00009167909,0.004445965],"study_design_scores_gemma":[0.000001909137,0.000001491336,0.0002970343,7.090649e-7,0.000001344053,0.000001476908,0.000001886808,0.999451,0.0001335843,0.00007225771,0.00003529427,0.000002005314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6211593,0.0001282456,0.3693315,0.0003565561,0.00004520688,0.00009781108,0.001984237,0.002444806,0.004452245],"genre_scores_gemma":[0.9623563,0.00004094599,0.03536752,0.00003926817,0.000006561181,0.00006567012,0.0008927798,0.00005185396,0.001179016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1538708,"threshold_uncertainty_score":0.3059504,"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."}}