{"id":"W2137681109","doi":"10.1071/aseg2013ab152","title":"Mass anomaly visualisation and depth estimation from full tensor gradient gravity data","year":2013,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"First Quantum Minerals (Canada)","funders":"","keywords":"Voxel; Geology; Gravitational field; Noise (video); Curvature; Tensor (intrinsic definition); Visualization; Gravity anomaly; Geodesy; Artificial intelligence; Geometry; Mathematics; Physics; Image (mathematics); Computer science; Paleontology","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":[0.0005391173,0.0008793658,0.0004394333,0.00343057,0.0001993572,0.001528869,0.0005768398,0.0008268933,0.002507856],"category_scores_gemma":[0.0034667,0.0002721596,0.0005999269,0.001759227,0.0003366813,0.000841214,0.001515827,0.0006226399,0.0009862136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003482153,"about_ca_system_score_gemma":0.0005385762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005768287,"about_ca_topic_score_gemma":0.006627763,"domain_scores_codex":[0.9996558,0.00005316214,0.0000204312,0.00005912222,0.0001490437,0.00006247571],"domain_scores_gemma":[0.9991942,0.0002458238,0.00009893082,0.0001117298,0.0002860767,0.00006314171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00140147,0.0001701197,0.03032015,0.0007979531,0.0001777973,0.0008775275,0.001747582,0.1855997,0.1709193,0.0108639,0.02590789,0.5712166],"study_design_scores_gemma":[0.00005868623,0.000100762,0.02229854,0.00008315262,0.00002689728,0.0003943242,0.000417047,0.9087076,0.04290183,0.008426277,0.01650057,0.00008430002],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3015757,0.0006494487,0.651508,0.0005908993,0.000171944,0.0001988288,0.01132997,0.02871755,0.005257745],"genre_scores_gemma":[0.6136187,0.0003149855,0.3738506,0.00005506424,0.00006269217,0.0001094342,0.008972196,0.001232172,0.001784174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005768287,"threshold_uncertainty_score":0.01146942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04034616593976033,"score_gpt":0.2513837001139851,"score_spread":0.2110375341742248,"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."}}