{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006269266,0.0003338012,0.0002887933,0.0006327378,0.0002260886,0.0007913788,0.0002398112,0.0007062716,0.0005991571],"category_scores_gemma":[0.001597926,0.0002125734,0.0001346835,0.0007192635,0.0007992713,0.0006849775,0.0003842847,0.0006103043,0.0003071246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002733631,"about_ca_system_score_gemma":0.0003652688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005410544,"about_ca_topic_score_gemma":0.001218975,"domain_scores_codex":[0.9995643,0.00008566064,0.00001452239,0.0001177358,0.0001881378,0.0000295823],"domain_scores_gemma":[0.9995593,0.000178751,0.0001352746,0.0000429401,0.00004754931,0.00003619632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001250001,0.00004566331,0.01054832,0.0002476607,0.00002617162,0.0001706864,0.0002044685,0.001906239,0.9231109,0.00509901,0.0003982253,0.05811762],"study_design_scores_gemma":[0.00002009649,0.0002299305,0.02270893,0.00006846572,0.00002517599,0.0007002177,0.0005173871,0.01574537,0.92434,0.01987821,0.01573086,0.00003549889],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8322717,0.006508984,0.1522705,0.001391376,0.0002135218,0.00006491344,0.001382405,0.000524407,0.005372212],"genre_scores_gemma":[0.8480443,0.005827938,0.140428,0.0003807868,0.00008285485,0.0000610177,0.001054597,0.00008128407,0.00403909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007913788,"threshold_uncertainty_score":0.003315508,"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."}}