{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003979858,0.000113761,0.0001070852,0.00006386597,0.00009750931,0.00008497748,0.0002925392,0.0000547901,0.000005551115],"category_scores_gemma":[0.0002270173,0.0001113402,0.00001901715,0.00007218309,0.00006036094,0.0000142325,0.0003993524,0.00005349985,0.000004025414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004436502,"about_ca_system_score_gemma":0.00004018554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000104902,"about_ca_topic_score_gemma":0.00008456661,"domain_scores_codex":[0.9989679,0.00003683414,0.0001382446,0.00054063,0.00008740242,0.0002290324],"domain_scores_gemma":[0.9990433,0.00003130061,0.00004435194,0.000767777,0.00002820417,0.00008510663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003892783,0.00003240379,0.002813286,0.00005786439,0.000009201136,0.000008951563,0.000009954709,0.000007082937,0.9926749,0.0005324586,0.002444192,0.001370785],"study_design_scores_gemma":[0.002679745,0.000373908,0.01295695,0.0005439476,0.0001462405,0.00005108587,0.0003358912,0.01536438,0.1649969,0.0004236148,0.8008494,0.00127794],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9545464,0.003408401,0.01020286,0.00003222854,0.0001381505,0.0002492114,0.03106469,0.00001232446,0.0003457606],"genre_scores_gemma":[0.9463183,0.0001529121,0.004536026,0.0001503211,0.00009348719,0.00001277759,0.04864261,0.00001463375,0.00007893569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.827678,"threshold_uncertainty_score":0.4540323,"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."}}