{"id":"W2107814573","doi":"10.1071/aseg2015ab213","title":"Improving modelling of AEM data affected by IP, two case studies","year":2015,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Inversion (geology); Geology; Electrical resistivity and conductivity; Anomaly (physics); Mineralogy; Geomorphology; Physics; Engineering; Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007817634,0.0001758438,0.0003207822,0.00005008591,0.00008949215,0.00003212929,0.0003279044,0.00005744962,0.00004476285],"category_scores_gemma":[0.0006046151,0.0001326095,0.0000439509,0.0002923812,0.00008609334,0.0003702725,0.00005620164,0.0002232508,0.00007555937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004551405,"about_ca_system_score_gemma":0.0000654952,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007798266,"about_ca_topic_score_gemma":0.0004979981,"domain_scores_codex":[0.9984095,0.0001388502,0.0003369717,0.0004128444,0.0003183508,0.0003835586],"domain_scores_gemma":[0.9984131,0.0005265448,0.0001801016,0.0004627039,0.0001294232,0.0002880728],"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":[0.00004231893,0.00009649876,0.00003698134,0.00003850925,0.00005090313,0.0004942503,0.000099639,0.0229123,0.0001192795,0.00001729423,0.001165355,0.9749267],"study_design_scores_gemma":[0.002242455,0.001122696,0.03583347,0.00006925161,0.0002681593,0.0008635379,0.001058098,0.9229518,0.01269037,0.02035142,0.001543164,0.001005647],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930512,0.002652329,0.002601509,0.00009642528,0.0002796525,0.0001746791,0.0002798559,0.00006646648,0.0007979107],"genre_scores_gemma":[0.9858989,0.00002719616,0.01358674,0.00006288192,0.0001201713,7.584738e-7,0.0001850143,0.000004792238,0.0001135092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.973921,"threshold_uncertainty_score":0.9988089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1320913649940368,"score_gpt":0.3273014608009427,"score_spread":0.1952100958069059,"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."}}