{"id":"W2138751395","doi":"10.1109/tgrs.2011.2132138","title":"Nonlinear Inversion for Multiple Objects in Transient Electromagnetic Induction Sensing of Unexploded Ordnance: Technique and Applications","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Nonlinear system; Initialization; Algorithm; Unexploded ordnance; Inversion (geology); Computer science; Principal component analysis; Polarization (electrochemistry); Mathematical optimization; Mathematics; Physics; Artificial intelligence; Geology","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":[0.0004001863,0.0003768688,0.0003110918,0.0003070163,0.0002727757,0.0004151212,0.0005822653,0.0005008755,0.0006308248],"category_scores_gemma":[0.0007856206,0.0002354279,0.0002860092,0.0004063844,0.0007491698,0.0007448547,0.0008634051,0.0006528405,0.00022934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000404646,"about_ca_system_score_gemma":0.0006378927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001650298,"about_ca_topic_score_gemma":0.002420023,"domain_scores_codex":[0.999837,0.00002629301,0.000005120158,0.00003768186,0.00008025552,0.00001365473],"domain_scores_gemma":[0.9998229,0.0000712694,0.00002817151,0.00003109559,0.00003636435,0.00001021025],"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":[0.0001022189,0.00009685873,0.003312613,0.0001550536,0.00004062214,0.0004334871,0.0005073152,0.5466782,0.1767184,0.0365575,0.0006874958,0.2347101],"study_design_scores_gemma":[0.000005615187,0.00002982079,0.0003683188,0.000005391695,0.000004629916,0.00008907295,0.00003095497,0.9722631,0.02055414,0.005241552,0.001392603,0.00001484388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02089966,0.00005925785,0.9778751,0.0001178064,0.000008977906,0.0000164188,0.00001317074,0.0001171521,0.0008924106],"genre_scores_gemma":[0.3329989,0.0001810131,0.6641167,0.00006335062,0.0000262475,0.00006272247,0.00005994314,0.00005679808,0.002434324],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001650298,"threshold_uncertainty_score":0.003281355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02335272153408986,"score_gpt":0.2317567526339926,"score_spread":0.2084040310999027,"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."}}