{"id":"W3046303573","doi":"10.1016/j.funbio.2020.07.007","title":"Low genetic differentiation between apotheciate Usnea florida and sorediate Usnea subfloridana (Parmeliaceae, Ascomycota) based on microsatellite data","year":2020,"lang":"en","type":"article","venue":"Fungal Biology","topic":"Lichen and fungal ecology","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Swiss Federal Institute for Forest, Snow and Landscape Research; Česká Zemědělská Univerzita v Praze; Eesti Teadusagentuur; Natural History Museum; European Commission; Tartu Ülikool; Department of Health and Social Care; University of South Alabama; Eidgenössische Technische Hochschule Zürich; Innovation Saskatchewan; Eastern Washington University","keywords":"Biology; Parmeliaceae; Taxon; Microsatellite; Evolutionary biology; Genus; Ecology; Ascomycota; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0002218961,0.0001781806,0.0001841702,0.001745001,0.0005295756,0.0004705412,0.000212888,0.000200494,0.002111397],"category_scores_gemma":[0.0008106808,0.00008425773,0.0001529187,0.0006951441,0.0003469533,0.0002377763,0.0003917748,0.0002008429,0.0001574613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002963134,"about_ca_system_score_gemma":0.0002715466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01174702,"about_ca_topic_score_gemma":0.04713133,"domain_scores_codex":[0.9997388,0.00004187091,0.00002034582,0.0001032448,0.00004199515,0.00005373906],"domain_scores_gemma":[0.9993109,0.0001377837,0.0001770502,0.00005066115,0.0001794091,0.0001442658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007698743,0.00007382462,0.6855275,0.0000899155,0.0002151325,0.00009754186,0.002373944,0.0001779486,0.2788451,0.0003392021,0.0001364171,0.03135372],"study_design_scores_gemma":[0.000007321285,0.00006497763,0.9964592,0.000009724764,0.00003409553,0.0001289353,0.0004864667,0.0002935094,0.001892628,0.00005976624,0.0005570795,0.000006203055],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999113,0.0000406,0.0001750036,0.00000524292,0.000001227012,0.00000286994,0.00008832465,0.000005053472,0.0005686934],"genre_scores_gemma":[0.9993967,0.00001465062,0.0002677785,0.00001043796,9.809519e-7,0.00000292464,0.0001572823,0.000002131683,0.0001471579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01174702,"threshold_uncertainty_score":0.02335733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04414765118410902,"score_gpt":0.2312416483757757,"score_spread":0.1870939971916667,"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."}}