{"id":"W2984480460","doi":"10.1080/22020586.2019.12072949","title":"Use of machine learning techniques on airborne geophysical data for mineral resources exploration in Burkina Faso","year":2019,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petro Geotech (Canada)","funders":"","keywords":"Prospectivity mapping; Mineral resource classification; Mineral exploration; Uranium; Artificial intelligence; Mining engineering; Computer science; Geology; Remote sensing; Geophysics; Geochemistry","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":[0.001107075,0.000428566,0.0001940421,0.002036634,0.0005460641,0.001139944,0.0002696079,0.0002890429,0.0008420448],"category_scores_gemma":[0.003004526,0.00009744491,0.0002323864,0.00187132,0.0002256417,0.0004698239,0.0005260778,0.0003172162,0.0002497368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007827436,"about_ca_system_score_gemma":0.0007362237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07295105,"about_ca_topic_score_gemma":0.08037955,"domain_scores_codex":[0.9995161,0.0002380991,0.00003614508,0.0000692639,0.00007528082,0.00006510247],"domain_scores_gemma":[0.9984633,0.0008354068,0.0001582284,0.0001144038,0.0003650729,0.00006374934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003456858,0.0003085858,0.4638565,0.0001968975,0.000249863,0.0009283263,0.0009921396,0.1519138,0.01685647,0.001406931,0.001841567,0.3611031],"study_design_scores_gemma":[0.00004575335,0.0001502632,0.3330139,0.0001304844,0.00009365639,0.0003171081,0.003566623,0.6363988,0.01652529,0.00237175,0.007326801,0.00005965302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845811,0.0004533732,0.009156727,0.0004978123,0.00002383708,0.00004397707,0.00105314,0.0002477841,0.003942286],"genre_scores_gemma":[0.9863284,0.0001608952,0.01250496,0.00002011187,0.000008561301,0.00001436983,0.0005500353,0.000008997933,0.0004036278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07295105,"threshold_uncertainty_score":0.1450529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04555429725939691,"score_gpt":0.2695622610416103,"score_spread":0.2240079637822134,"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."}}