{"id":"W2618219841","doi":"10.1016/j.jenvrad.2017.05.006","title":"Airborne gamma-ray spectrometry data processing using 1.5D inversion","year":2017,"lang":"en","type":"article","venue":"Journal of Environmental Radioactivity","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Maple Leaf Foods","funders":"","keywords":"Inversion (geology); Data processing; Terrain; Remote sensing; Inverse problem; Inverse; Footprint; Range (aeronautics); Environmental science; Algorithm; Computer science; Geodesy; Geology; Mathematics; Aerospace engineering; Geography; Engineering; Cartography; Geometry; Database","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":[0.0004663608,0.00060852,0.0005566571,0.0007540258,0.0004666047,0.0009781842,0.0007337074,0.0008765658,0.003394384],"category_scores_gemma":[0.001609183,0.0004766608,0.0007988501,0.000926365,0.0003156381,0.000725642,0.001201243,0.0008960731,0.001526467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004074715,"about_ca_system_score_gemma":0.001866803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005515558,"about_ca_topic_score_gemma":0.005853797,"domain_scores_codex":[0.9997521,0.00003728617,0.00002408731,0.00004140228,0.0001214828,0.00002368275],"domain_scores_gemma":[0.9996184,0.0001055117,0.00003245916,0.00006589617,0.0001657464,0.00001193828],"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":[0.0002076204,0.0001831087,0.002716398,0.000241096,0.0001158137,0.000165153,0.0001854232,0.4523492,0.07204967,0.01841363,0.005284183,0.4480886],"study_design_scores_gemma":[0.00001381992,0.00001781318,0.0005012496,0.000007873502,0.000009867375,0.00006841181,0.00001821481,0.9803225,0.01243649,0.002476827,0.004110338,0.00001650654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00993705,0.00006502554,0.9867688,0.00009182357,0.00004164676,0.00004153748,0.0001937174,0.001537126,0.001323166],"genre_scores_gemma":[0.07740146,0.0000862655,0.9201975,0.00005713706,0.00001836821,0.00009603497,0.0006116624,0.0002375796,0.001293861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005515558,"threshold_uncertainty_score":0.0113554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1171865339572225,"score_gpt":0.3437176009858236,"score_spread":0.2265310670286012,"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."}}