{"id":"W2987055262","doi":"10.1080/22020586.2019.12072950","title":"Application of growing-body potential-field inversion from drillholes","year":2019,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mira Geoscience (Canada)","funders":"","keywords":"Inversion (geology); Physical property; Geology; Computer science; Property (philosophy); Potential field; Inverse transform sampling; Geophysics; Algorithm; Physics; Paleontology; Telecommunications","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":[0.0006376107,0.0005047017,0.0003929811,0.0005793209,0.0004098561,0.0006986473,0.001111663,0.000631644,0.003913134],"category_scores_gemma":[0.003558209,0.0002984936,0.0003819881,0.0004119566,0.0003270024,0.0006114592,0.0009999502,0.0005792064,0.0006068074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003235127,"about_ca_system_score_gemma":0.0006076657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004804636,"about_ca_topic_score_gemma":0.004730279,"domain_scores_codex":[0.9998136,0.00005095733,0.00001028837,0.00002899089,0.00007383645,0.00002230592],"domain_scores_gemma":[0.9993135,0.0003439072,0.00004075029,0.000114004,0.0001618793,0.00002601837],"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":[0.0001255793,0.00004426236,0.00256941,0.0001040397,0.00004145972,0.0003465878,0.0002308037,0.8399907,0.0203091,0.0133805,0.002758309,0.1200993],"study_design_scores_gemma":[0.000006189923,0.000009140003,0.0001695274,0.000004086805,0.000001884133,0.00002839967,0.00001488587,0.9922295,0.003961298,0.002664239,0.0009054383,0.000005404418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07571197,0.00004810423,0.9100054,0.0001427505,0.00003686528,0.00007727755,0.0003512691,0.003977176,0.009649195],"genre_scores_gemma":[0.5680278,0.00003202425,0.4283779,0.00004000471,0.00001290528,0.00006288513,0.0005834493,0.0006872336,0.002175843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004804636,"threshold_uncertainty_score":0.01309073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005388720598843486,"score_gpt":0.2163241469935759,"score_spread":0.2109354263947324,"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."}}