{"id":"W1993679825","doi":"10.1093/database/bau061","title":"Finding needles in haystacks: linking scientific names, reference specimens and molecular data for Fungi","year":2014,"lang":"en","type":"article","venue":"Database","topic":"Plant Pathogens and Fungal Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":481,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Biodiversity Monitoring Institute","funders":"National Human Genome Research Institute; Agricultural Research Service; College of Pharmacy, University of Michigan; National Institutes of Health; National Brain Research Centre; University of Michigan; Ministry of Agriculture, Forestry and Fisheries; U.S. National Library of Medicine; University of Alberta; Beef Cattle Research Council; Université Catholique de Louvain","keywords":"RefSeq; Biology; Phylogenetic tree; Identification (biology); DNA sequencing; Cistron; Computational biology; Ribosomal DNA; Internal transcribed spacer; Information retrieval; Genome; Computer science; Genetics; DNA; Gene; Ecology","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":[0.0354096,0.002029373,0.002514855,0.02970178,0.004344898,0.01446067,0.003854073,0.003963175,0.01679552],"category_scores_gemma":[0.1372806,0.002392008,0.001689758,0.02547719,0.002378234,0.02084536,0.01368455,0.003936099,0.01668761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002479961,"about_ca_system_score_gemma":0.006900495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00483829,"about_ca_topic_score_gemma":0.007699289,"domain_scores_codex":[0.9836568,0.006111185,0.004071822,0.002340534,0.003340852,0.0004788711],"domain_scores_gemma":[0.9075709,0.03733968,0.01311451,0.02733636,0.01151456,0.003124069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001159429,0.0004097787,0.03471677,0.009298661,0.00065739,0.00337705,0.008696296,0.001382406,0.02132368,0.03180118,0.2028865,0.6842908],"study_design_scores_gemma":[0.00007837881,0.0001655619,0.01907069,0.006161178,0.0004640438,0.00283544,0.004722209,0.005141701,0.01205278,0.06583986,0.8829799,0.0004880802],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04364848,0.02648981,0.7472098,0.01893948,0.009104189,0.001737745,0.08167106,0.04565572,0.02554382],"genre_scores_gemma":[0.04560797,0.01014057,0.8190668,0.003777487,0.0009648668,0.0006763758,0.1034077,0.008005426,0.008352903],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0354096,"threshold_uncertainty_score":0.1872661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04203084470135757,"score_gpt":0.282761934017969,"score_spread":0.2407310893166114,"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."}}