{"id":"W3120950118","doi":"10.22215/etd/2017-11801","title":"Correcting airborne gravity data for overburden thickness using airborne transient electromagnetic data","year":2017,"lang":"en","type":"dissertation","venue":"","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Overburden; Bedrock; Geology; Bouguer anomaly; Bathymetry; Gravity anomaly; Digital elevation model; Inversion (geology); Geodetic datum; Geomorphology; Terrain; Geodesy; Depth sounding; Borehole; Elevation (ballistics); Seismology; Remote sensing; Mining engineering; Geotechnical engineering; Tectonics; Cartography; Engineering; Geography; Petroleum engineering","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":[0.000241435,0.0005059072,0.0002557895,0.001131479,0.0003004688,0.0008577417,0.000481431,0.0002528775,0.001486274],"category_scores_gemma":[0.001069114,0.000185363,0.0003074419,0.001473394,0.0001896351,0.0004491142,0.0005715109,0.0004864596,0.0007972316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002554983,"about_ca_system_score_gemma":0.0006140689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006137039,"about_ca_topic_score_gemma":0.01177101,"domain_scores_codex":[0.9997756,0.00002027306,0.00001082321,0.00006741499,0.0001044027,0.00002154228],"domain_scores_gemma":[0.9996552,0.00004987207,0.00005386135,0.00009713627,0.0001323147,0.00001157379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00004455359,0.00006186285,0.02385688,0.0001366389,0.0000954132,0.000174714,0.0004229391,0.03993398,0.06660884,0.004471133,0.004549451,0.8596436],"study_design_scores_gemma":[0.00007838462,0.0002695632,0.2021989,0.0001356728,0.0002129307,0.00134661,0.00160998,0.498551,0.1747858,0.0149316,0.1056519,0.0002275557],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1182099,0.0002517779,0.8726144,0.0001273646,0.0001843764,0.00006152332,0.0009959358,0.002828445,0.00472628],"genre_scores_gemma":[0.407322,0.0004712945,0.5829401,0.00004963198,0.00007332843,0.00006823101,0.002539488,0.0004852599,0.006050643],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006137039,"threshold_uncertainty_score":0.01220262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07796305545418847,"score_gpt":0.3365333528544294,"score_spread":0.2585702974002409,"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."}}