{"id":"W2335687722","doi":"10.1190/1.3513660","title":"Inversion of Multi‐source TEM data with Applications to Mt. Milligan","year":2010,"lang":"en","type":"article","venue":"","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Inversion (geology); Discretization; Algorithm; Computer science; Matrix decomposition; Inverse problem; Synthetic data; Iterative method; Computational science; Geology; Mathematics; Mathematical analysis; Physics","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":[0.0001625705,0.0002919409,0.0001694694,0.0004517736,0.0004572004,0.0005500978,0.0005250916,0.0003587577,0.00127003],"category_scores_gemma":[0.0008395431,0.0001649213,0.0001395084,0.0008194303,0.0001989294,0.0002210601,0.000407557,0.0002731221,0.0001864879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007800448,"about_ca_system_score_gemma":0.0007729745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07887084,"about_ca_topic_score_gemma":0.1861789,"domain_scores_codex":[0.9999284,0.000009802657,0.000003791419,0.00001614912,0.00003005649,0.00001178992],"domain_scores_gemma":[0.9998372,0.00004915504,0.00002506723,0.00002784715,0.00004713527,0.00001371086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003512287,0.000143983,0.06285748,0.0002320933,0.00006851475,0.003491604,0.00145101,0.5504382,0.1680113,0.005864269,0.00639341,0.2006969],"study_design_scores_gemma":[0.00005778268,0.00009652039,0.05965068,0.0000206656,0.00002319217,0.0005355858,0.000791557,0.8771337,0.05029915,0.001654296,0.009683628,0.00005331247],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9263021,0.0001215846,0.05978879,0.0005438891,0.00002835621,0.00006948163,0.001183035,0.00122249,0.01074011],"genre_scores_gemma":[0.9552215,0.00004226562,0.04296546,0.00002266455,0.000007080303,0.00001846121,0.0004456126,0.00005160547,0.001225363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07887084,"threshold_uncertainty_score":0.1568235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02975573397301037,"score_gpt":0.2634946121747831,"score_spread":0.2337388782017727,"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."}}