{"id":"W2972511081","doi":"10.1590/1678-4685-gmb-2018-0140","title":"Gene pool sharing and genetic bottleneck effects in subpopulations of Eschweilera ovata (Cambess.) Mart. ex Miers (Lecythidaceae) in the Atlantic Forest of southern Bahia, Brazil","year":2019,"lang":"en","type":"article","venue":"Genetics and Molecular Biology","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Centre","funders":"Fundação de Amparo à Pesquisa do Estado da Bahia; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Instituto Chico Mendes de Conservação da Biodiversidade; Integrated Envirotech Sdn Bhd; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Biology; Gene flow; Genetic diversity; Population bottleneck; Microsatellite; Genetic structure; Forest fragmentation; Fragmentation (computing); Ecology; Gene pool; Evolutionary biology; Biodiversity; Genetic variation; Genetics; Population; Gene; Allele","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001858559,0.0001845339,0.000273759,0.0001272353,0.00003135992,0.00001323509,0.0002385853,0.0002502352,0.000008936909],"category_scores_gemma":[0.00003866283,0.0001630852,0.00006133519,0.0001524457,0.0001485128,0.000002501745,0.0002131528,0.0001083408,0.000001853034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004857024,"about_ca_system_score_gemma":0.00003178356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002876754,"about_ca_topic_score_gemma":0.0005214292,"domain_scores_codex":[0.9987635,0.0001487564,0.0003337654,0.0004035358,0.0001004641,0.000249986],"domain_scores_gemma":[0.9993352,0.00002814166,0.0001364802,0.0004027447,0.00004813167,0.0000493594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004425665,0.0000232476,0.7114527,0.00007174649,0.0000296366,0.00000501197,0.000187778,0.001302642,0.2859949,0.0002171757,0.00000748056,0.0006634559],"study_design_scores_gemma":[0.001975355,0.0005622864,0.9539679,0.00004316058,0.00006718958,0.0000496805,0.0002588119,0.001467621,0.03833186,0.00231068,0.0006318255,0.0003336825],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956647,0.00274444,0.0009245708,0.00007616587,0.00007257342,0.0004179339,0.00003796897,0.000002184395,0.00005951036],"genre_scores_gemma":[0.9976838,0.0003782465,0.001502798,0.0002086833,0.00002074069,0.000006720993,0.0001594196,0.00001592491,0.00002363373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2476631,"threshold_uncertainty_score":0.6650423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005238674261932336,"score_gpt":0.2280956277005322,"score_spread":0.2228569534385999,"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."}}