{"id":"W3191288094","doi":"10.20944/preprints202108.0067.v1","title":"3D Geophysical Post-Inversion Feature Extraction for Mineral Exploration Through Fast-ICA","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":1,"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); Feature extraction; Geophysics; Physical property; Property (philosophy); Geologic map; Mineral exploration; Feature (linguistics); Induced polarization; Pattern recognition (psychology); Computer science; Artificial intelligence; Data mining; Seismology; Electrical resistivity and conductivity; Tectonics; Geomorphology; Materials science","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":[0.0002955322,0.0008136155,0.0004275462,0.001044901,0.0002845706,0.000671146,0.0005938157,0.0005136565,0.001425341],"category_scores_gemma":[0.0008881916,0.0004063724,0.001081452,0.0009302659,0.0003641569,0.0008101124,0.0006183108,0.0007907919,0.0005407798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003881434,"about_ca_system_score_gemma":0.0010945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003955635,"about_ca_topic_score_gemma":0.005057819,"domain_scores_codex":[0.9999018,0.0000159899,0.000004579955,0.00001835247,0.0000428623,0.00001643914],"domain_scores_gemma":[0.9997779,0.00007526965,0.00003372925,0.0000387424,0.00006411165,0.00001030383],"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":[0.0001272242,0.00007939289,0.002683056,0.0001534314,0.0001064999,0.0002030932,0.0001464775,0.5794533,0.07728754,0.008692785,0.002620377,0.3284467],"study_design_scores_gemma":[0.00000435142,0.00000879209,0.0005567601,0.000003135966,0.00000841968,0.00003184188,0.000009519488,0.9917343,0.005181212,0.001618581,0.0008333263,0.000009715523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02232782,0.0000885884,0.9752099,0.00008618252,0.00001898132,0.00002381529,0.000150575,0.001323963,0.0007701647],"genre_scores_gemma":[0.2780502,0.0002154704,0.7195926,0.00004586501,0.00002630091,0.0001068662,0.0006039381,0.0002264276,0.00113223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003955635,"threshold_uncertainty_score":0.007865191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1040687802258447,"score_gpt":0.3602724034280822,"score_spread":0.2562036232022375,"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."}}