{"id":"W3196705509","doi":"10.3390/min11090959","title":"3D Geophysical Post-Inversion Feature Extraction for Mineral Exploration through Fast-ICA","year":2021,"lang":"en","type":"article","venue":"Minerals","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Ministère de l'Énergie et des Ressources Naturelles","keywords":"Geology; Inversion (geology); Geophysics; Feature extraction; Physical property; Induced polarization; Property (philosophy); Mineral exploration; Feature (linguistics); Maximization; Geologic map; Computer science; Pattern recognition (psychology); Artificial intelligence; Seismology; Electrical resistivity and conductivity; Tectonics; Mathematics; Geomorphology; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002983775,0.0008122459,0.0004249235,0.001047802,0.000285375,0.0006699408,0.0005907625,0.0005104148,0.001401303],"category_scores_gemma":[0.0008860531,0.0004092228,0.001081112,0.0009283159,0.0003643435,0.0008103687,0.0006119894,0.0007905174,0.0005295401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003902685,"about_ca_system_score_gemma":0.001098474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003995324,"about_ca_topic_score_gemma":0.005119944,"domain_scores_codex":[0.9999017,0.00001592499,0.000004605458,0.00001842405,0.00004290695,0.0000164717],"domain_scores_gemma":[0.9997786,0.0000748334,0.00003359112,0.00003852167,0.0000642387,0.00001026772],"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":[0.0001275839,0.00007952352,0.002739647,0.0001542095,0.0001070436,0.0002047052,0.0001502603,0.5796942,0.07857075,0.008678515,0.002578412,0.3269151],"study_design_scores_gemma":[0.00000434069,0.000008858976,0.0005628424,0.000003112582,0.000008511034,0.00003173338,0.000009676464,0.9916992,0.005254537,0.001587044,0.0008204161,0.000009787848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02304479,0.00008792085,0.9744965,0.00008569346,0.00001901908,0.0000240274,0.0001491111,0.00132592,0.0007669441],"genre_scores_gemma":[0.280733,0.0002123848,0.7169538,0.00004499144,0.00002572292,0.0001056999,0.0005866488,0.0002227959,0.001114915],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003995324,"threshold_uncertainty_score":0.007944167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02853691889571029,"score_gpt":0.2999526384900906,"score_spread":0.2714157195943803,"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."}}