{"id":"W3205087287","doi":"10.25959/100.00037833","title":"Machine learning for mineral exploration: prediction and quantified uncertainty at multiple exploration stages","year":2021,"lang":"en","type":"dissertation","venue":"UTAS Research Repository","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mineral exploration; Variety (cybernetics); Context (archaeology); Geologist; Machine learning; Computer science; Artificial intelligence; Ground truth; Data mining; Data science; Geology; Geophysics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009892892,0.001075026,0.001522158,0.002044605,0.001023217,0.004723175,0.002049548,0.002214575,0.001827746],"category_scores_gemma":[0.03230521,0.001047468,0.001236023,0.002048854,0.004196553,0.008144299,0.004023172,0.00478267,0.0002896352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003011032,"about_ca_system_score_gemma":0.001943671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004480863,"about_ca_topic_score_gemma":0.002932508,"domain_scores_codex":[0.9963148,0.001661475,0.0001932268,0.0006611292,0.0009275994,0.0002418023],"domain_scores_gemma":[0.967814,0.02755537,0.001630532,0.001307255,0.001367812,0.0003249367],"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":[0.0001156339,0.00009500806,0.009926054,0.0003318127,0.000152752,0.0001645902,0.0004555984,0.6925796,0.0005495977,0.2197312,0.00238201,0.07351608],"study_design_scores_gemma":[0.000005479254,0.00002686302,0.0007011509,0.00007522485,0.0000106597,0.00002448074,0.0000647064,0.7841133,0.0003011945,0.213285,0.001366254,0.00002556537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04718982,0.007394867,0.932312,0.005758799,0.0001506751,0.0001610989,0.0003266534,0.0002746074,0.006431599],"genre_scores_gemma":[0.697249,0.005531158,0.2906707,0.0005925126,0.0006668282,0.0004570606,0.000435756,0.0001344126,0.004262595],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009892892,"threshold_uncertainty_score":0.05231929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08437352356715794,"score_gpt":0.3312068648164221,"score_spread":0.2468333412492641,"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."}}