{"id":"W4401171049","doi":"10.1016/j.gexplo.2024.107555","title":"Trace element signatures in scheelite associated with various deposit types: A tool for mineral targeting","year":2024,"lang":"en","type":"article","venue":"Journal of Geochemical Exploration","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Nova Scotia Department of Energy and Mines; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Scheelite; Hydrothermal circulation; Geology; Geochemistry; Skarn; Trace element; Phlogopite; Mineral; Metamorphic rock; Mineralogy; Fluid inclusions; Chemistry; Mantle (geology)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006320464,0.000108064,0.0001672759,0.00007884298,0.00003880905,0.0001543001,0.0002237412,0.00009859547,0.000007236512],"category_scores_gemma":[0.000584918,0.0000823096,0.00007041606,0.0003093499,0.00001419424,0.0006403376,0.00003316131,0.000286114,9.294773e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009132226,"about_ca_system_score_gemma":0.00009078094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002421081,"about_ca_topic_score_gemma":0.000004374673,"domain_scores_codex":[0.9989506,0.00003693555,0.0003964592,0.0001819692,0.0002400715,0.0001939352],"domain_scores_gemma":[0.9992467,0.0001969634,0.0001680986,0.00008496367,0.0002588599,0.00004447648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003690149,0.0005204252,0.001298484,0.0004098896,0.0002959965,0.0004510258,0.00591647,0.06617682,0.8895042,0.006911242,0.01658818,0.01155823],"study_design_scores_gemma":[0.002069675,0.0008780728,0.000643258,0.001463204,0.00008266867,0.0000886671,0.0002879152,0.7390057,0.2126199,0.02595183,0.01632203,0.0005870714],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4398461,0.00165249,0.5502003,0.007389023,0.0002696082,0.0002517366,0.000003077493,0.00006990365,0.0003178353],"genre_scores_gemma":[0.9782338,0.00001432798,0.02134845,0.00008737714,0.0001518127,0.00001472928,0.00001130137,0.00000340168,0.0001348406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6768843,"threshold_uncertainty_score":0.3356488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01263774829381224,"score_gpt":0.2357287994063614,"score_spread":0.2230910511125491,"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."}}