{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002689863,0.0001574813,0.0002555667,0.0009097717,0.0003786036,0.0003177662,0.0002215653,0.0001230148,0.0004708035],"category_scores_gemma":[0.0005206274,0.0001103743,0.0001643147,0.000405943,0.0003199955,0.0001758358,0.0003158609,0.000132992,0.00005961681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002830423,"about_ca_system_score_gemma":0.0002078724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01094757,"about_ca_topic_score_gemma":0.0286299,"domain_scores_codex":[0.99985,0.00002767912,0.00001253477,0.00005915226,0.00002364037,0.0000269781],"domain_scores_gemma":[0.9997739,0.00004739729,0.00008248798,0.00002067806,0.0000435991,0.00003187057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001955997,0.00007084847,0.9282276,0.00003470392,0.00013819,0.0001632619,0.003646969,0.0001884569,0.05548235,0.0001802648,0.00003554364,0.0116362],"study_design_scores_gemma":[0.000005823875,0.00005227221,0.9979996,0.000006142913,0.00002513198,0.0001146387,0.0007245078,0.0003982195,0.0004482589,0.00005221003,0.0001691481,0.000003931651],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999804,0.00004412357,0.00004900523,0.000002426905,3.444376e-7,0.000001851517,0.00001779139,9.33412e-7,0.00007958643],"genre_scores_gemma":[0.9998056,0.00002106289,0.00007610537,0.000002006475,5.185132e-7,0.000002832797,0.00005481304,6.432264e-7,0.00003641859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01094757,"threshold_uncertainty_score":0.02176768,"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."}}