{"id":"W2043046600","doi":"10.1071/aseg2015ab070","title":"Combining Machine Learning and Geophysical Inversion for Applied Geophysics","year":2015,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mira Geoscience (Canada)","funders":"U.S. Geological Survey","keywords":"Inversion (geology); Geophysics; Geology; Lithology; Probabilistic logic; Exploration geophysics; Set (abstract data type); Machine learning; Computer science; Artificial intelligence; Seismology; Petrology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003021242,0.0001528615,0.0001767054,0.00002975487,0.0001666048,0.00009051134,0.0002626723,0.00007839128,0.000001888164],"category_scores_gemma":[0.0001733207,0.0001463885,0.00004046969,0.0001161617,0.00004951292,0.0002057066,0.0002259487,0.00023091,0.00002588304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001706175,"about_ca_system_score_gemma":0.00004906048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002601803,"about_ca_topic_score_gemma":0.000001366545,"domain_scores_codex":[0.9989379,0.00001914277,0.0001744484,0.0003798451,0.0001843496,0.0003043239],"domain_scores_gemma":[0.9992058,0.0001626408,0.0001225066,0.0002104954,0.00009659305,0.0002019977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002089548,0.0006864265,0.000447303,0.0002888533,0.0001127475,0.000114689,0.003247385,0.004324462,0.02509419,0.3085912,0.004354698,0.6525291],"study_design_scores_gemma":[0.006967029,0.0008724074,0.04334587,0.0001062929,0.00006603835,0.00009695003,0.0008725292,0.2768227,0.06314199,0.4423206,0.1637353,0.001652255],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8020574,0.0003472458,0.07507049,0.006391505,0.0009179124,0.001108991,0.00001408227,0.001201257,0.1128911],"genre_scores_gemma":[0.9893587,0.000003909932,0.009617183,0.0001840646,0.00008743355,0.00001624972,0.00002957427,0.000004476047,0.0006984588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6508768,"threshold_uncertainty_score":0.5969551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02068309342190532,"score_gpt":0.2371564969375014,"score_spread":0.2164734035155961,"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."}}