{"id":"W1970575700","doi":"10.1371/journal.pone.0103899","title":"DNA Barcodes and Species Distribution Models Evaluate Threats of Global Climate Changes to Genetic Diversity: A Case Study from Nanorana parkeri (Anura: Dicroglossidae)","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Ontario Museum","funders":"Chinese Academy of Sciences; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Biodiversity; Genetic diversity; DNA barcoding; Ecology; Biology; Lineage (genetic); Refugium (fishkeeping); Plateau (mathematics); Environmental niche modelling; Intraspecific competition; Climate change; Species distribution; Ecological niche; Habitat; Evolutionary biology; Geography; Population; Genetics","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":[0.001635383,0.0004397438,0.0002167979,0.001192507,0.0005416707,0.0005768066,0.0005677108,0.0005815928,0.001221076],"category_scores_gemma":[0.003392836,0.0001535226,0.0005904507,0.001163261,0.0004000649,0.0008463834,0.0004213618,0.0003421284,0.0002085418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001725791,"about_ca_system_score_gemma":0.0005674928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03533405,"about_ca_topic_score_gemma":0.05195175,"domain_scores_codex":[0.9996456,0.0001519236,0.0000155239,0.0000889656,0.00006091369,0.00003715577],"domain_scores_gemma":[0.9982367,0.001121629,0.0002523804,0.00008100388,0.0002129435,0.00009537119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001578779,0.0001675741,0.6920672,0.0001482078,0.0001841119,0.001269434,0.0007895401,0.2284242,0.005081213,0.003911541,0.00206923,0.06572989],"study_design_scores_gemma":[0.00001941327,0.0001857452,0.1360972,0.0000372473,0.00006204016,0.0004501853,0.000858455,0.8542288,0.002360995,0.003473955,0.002176129,0.00004994889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798995,0.0002357081,0.01633232,0.0003250455,0.00001120864,0.00003972157,0.000798237,0.0001781748,0.00218004],"genre_scores_gemma":[0.978658,0.0001434345,0.0197533,0.0000434054,0.000006159178,0.00001844478,0.0007867611,0.00003434095,0.0005561786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03533405,"threshold_uncertainty_score":0.07025677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05112199110516148,"score_gpt":0.2490048565042906,"score_spread":0.1978828653991291,"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."}}