{"id":"W4390664395","doi":"10.1016/j.gexplo.2024.107388","title":"Using machine learning to identify indicators of rare earth element enrichment in sedimentary strata with applications for metal prospectivity","year":2024,"lang":"en","type":"article","venue":"Journal of Geochemical Exploration","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Geology; Prospectivity mapping; Sedimentary rock; Rare-earth element; Geochemistry; Lithology; Earth science; Mining engineering; Rare earth; Structural basin; Geomorphology","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":[0.0008525411,0.0005026255,0.0004633644,0.001861296,0.0002749727,0.001084767,0.0004148819,0.0006473693,0.0006956172],"category_scores_gemma":[0.002242825,0.0002246941,0.0004833674,0.00106913,0.0003059845,0.0006446593,0.0004463213,0.0005141402,0.0002872909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002709022,"about_ca_system_score_gemma":0.0003395908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001913725,"about_ca_topic_score_gemma":0.002526357,"domain_scores_codex":[0.999775,0.00007085436,0.00001817088,0.000060214,0.00004511369,0.00003062759],"domain_scores_gemma":[0.99883,0.0006874979,0.0001690542,0.00006574237,0.0002089396,0.00003870962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006549669,0.001077175,0.2010222,0.0001608513,0.0003957975,0.0001970286,0.0001761371,0.1554018,0.04912593,0.001906756,0.0014472,0.5884341],"study_design_scores_gemma":[0.00001623703,0.0001294701,0.02498433,0.00001191902,0.00005078156,0.00006743554,0.00006129568,0.9632376,0.008969879,0.002016085,0.0004330831,0.00002192441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8395618,0.0006423746,0.1560491,0.0003230803,0.00005931022,0.0000468196,0.000284893,0.0004423325,0.002590188],"genre_scores_gemma":[0.9615082,0.000144918,0.0368569,0.00003850309,0.00003154204,0.00002244557,0.0001999114,0.00001268672,0.001184821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001913725,"threshold_uncertainty_score":0.004508734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02745584616012172,"score_gpt":0.3018181330282635,"score_spread":0.2743622868681418,"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."}}