{"id":"W2136986626","doi":"10.2113/jeeg16.3.127","title":"Robust Inversion of Time-domain Electromagnetic Data: Application to Unexploded Ordnance Discrimination","year":2011,"lang":"en","type":"article","venue":"Journal of Environmental and Engineering Geophysics","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Quest University Canada; University of British Columbia","funders":"Environmental Security Technology Certification Program; Strategic Environmental Research and Development Program","keywords":"Unexploded ordnance; Inversion (geology); Clutter; Outlier; Geology; Algorithm; Gaussian; Least-squares function approximation; Computer science; Geodesy; Radar; Mathematics; Statistics; Remote sensing; Physics; Seismology; Estimator","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":[0.000557958,0.0003841273,0.0002793193,0.0005109247,0.000183357,0.0004650694,0.0003979687,0.0003372195,0.0004897654],"category_scores_gemma":[0.002549218,0.0001225266,0.0001836206,0.0006326362,0.0003007585,0.00027734,0.0003930864,0.0002684637,0.0001500002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002110098,"about_ca_system_score_gemma":0.0004336168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005138546,"about_ca_topic_score_gemma":0.005162897,"domain_scores_codex":[0.9998024,0.00005430887,0.00001156128,0.00003857221,0.00007356615,0.00001962515],"domain_scores_gemma":[0.999331,0.0003107373,0.0000699298,0.00009332445,0.0001688792,0.0000261127],"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":[0.0004282595,0.0001628397,0.01262771,0.000103919,0.00009912805,0.0007219161,0.0002564633,0.5785664,0.1403732,0.003859169,0.0009892662,0.2618117],"study_design_scores_gemma":[0.00001668335,0.00003359537,0.003588384,0.000003870783,0.000007667329,0.00009104271,0.00005329134,0.9744702,0.01970387,0.001305054,0.0007083564,0.00001792016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5669659,0.0001379155,0.4296129,0.000206019,0.00003330281,0.00002698086,0.0001725706,0.0009868112,0.001857583],"genre_scores_gemma":[0.8412901,0.00005329541,0.1577769,0.0000394504,0.00001194143,0.000009893433,0.0001837934,0.00004665154,0.0005879803],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005138546,"threshold_uncertainty_score":0.01021725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01517489707982961,"score_gpt":0.182806358903801,"score_spread":0.1676314618239714,"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."}}