{"id":"W2219277403","doi":"10.1016/j.geothermics.2015.06.015","title":"Resistivity characterization of the Krafla and Hengill geothermal fields through 3D MT inverse modeling","year":2015,"lang":"en","type":"article","venue":"Geothermics","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":63,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Lawrence Berkeley National Laboratory; Basic Energy Sciences; Geothermal Technologies Program; U.S. Department of Energy","keywords":"Geology; Magnetotellurics; Geothermal gradient; Electrical resistivity and conductivity; Epidote; Magma; Volcano; Geophysics; Lithology; Mineralogy; Petrology; Chlorite; Geochemistry; Quartz; Paleontology","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":[0.0002108557,0.00008518063,0.000129831,0.00001369313,0.00006863963,0.00001480199,0.0001226955,0.0000713359,0.00008843552],"category_scores_gemma":[0.0000839536,0.00005495075,0.00003534569,0.0001682205,0.00006485423,0.0001235631,0.00001900702,0.0001174681,0.00001322228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002426779,"about_ca_system_score_gemma":0.00003036387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001577662,"about_ca_topic_score_gemma":0.0003017661,"domain_scores_codex":[0.9992905,0.0001199007,0.0001316537,0.0001421421,0.0001529507,0.0001628034],"domain_scores_gemma":[0.9996227,0.00006325744,0.00006221614,0.0001488875,0.00004160276,0.00006130693],"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":[0.0003448754,0.0001369646,0.06893424,0.000123345,0.00006890742,0.000006660445,0.003629761,0.1205767,0.004751643,0.0009724112,0.0001192389,0.8003353],"study_design_scores_gemma":[0.000287419,0.0001027986,0.1146453,0.00001640837,0.00002066143,0.000004175201,0.00004493097,0.8715501,0.0007158037,0.01057757,0.00187492,0.0001599111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918934,0.00007237774,0.00603957,0.0002984295,0.0001519933,0.00009133693,0.00002710776,0.00001552316,0.001410236],"genre_scores_gemma":[0.9978217,0.00003287287,0.001298332,0.0004701015,0.00008899203,5.138613e-7,0.000009012589,0.000003070397,0.0002753332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8001754,"threshold_uncertainty_score":0.2384963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03526575608742556,"score_gpt":0.2312254782558319,"score_spread":0.1959597221684064,"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."}}