{"id":"W4291318754","doi":"10.31223/x5qm04","title":"DNA sequencing, microbial sensors, and the discovery of buried mineral resources","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overburden; Kimberlite; Surface mining; Mineral resource classification; Environmental DNA; Mining engineering; Amplicon sequencing; DNA sequencing; Earth science; Mineral exploration; Amplicon; Mineralization (soil science); Geology; Geochemistry; Ecology; Biology; Soil science; Geography; Biodiversity; DNA; Paleontology; Soil water; Coal mining; Gene; Polymerase chain reaction; Archaeology; Coal; Genetics","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":[0.000318537,0.0002373093,0.0003906014,0.00009186535,0.00009566025,0.000131352,0.000240177,0.0001172966,0.00008430697],"category_scores_gemma":[0.00003367148,0.0001635269,0.0001105663,0.0000624616,0.0001523689,0.00005988924,0.0004733423,0.0005826072,8.121179e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006264194,"about_ca_system_score_gemma":0.00003140736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004676962,"about_ca_topic_score_gemma":0.00003310782,"domain_scores_codex":[0.9990081,0.00006264855,0.0003229338,0.0002448091,0.0001655415,0.0001960244],"domain_scores_gemma":[0.9994741,0.00009726031,0.00008926545,0.0002927455,0.00001641743,0.0000302224],"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":[0.0002913379,0.00003043842,0.0007068953,0.004378728,0.0007162764,0.00003812372,0.02484858,0.7694029,0.1623764,0.002365378,0.03395348,0.0008914557],"study_design_scores_gemma":[0.0175765,0.0003076355,0.003682218,0.004070675,0.002076127,0.0005463731,0.02032464,0.6595522,0.128465,0.01726913,0.1369909,0.009138565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852405,0.0009883253,0.0001299204,0.000176071,0.0004433969,0.0001574255,0.00005343731,0.0001419583,0.01266901],"genre_scores_gemma":[0.990776,0.00008260788,0.0004925444,0.00003896504,0.0003154625,0.0000173698,0.00003345213,0.00003931973,0.008204273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1098507,"threshold_uncertainty_score":0.6668434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01222747505181061,"score_gpt":0.2110608577844957,"score_spread":0.1988333827326851,"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."}}