{"id":"W2463299080","doi":"10.1038/srep28828","title":"Historical and contemporary factors generate unique butterfly communities on islands","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ministerio de Economía y Competitividad; European Commission","keywords":"Biological dispersal; Ecology; Butterfly; Biology; Genetic structure; Species richness; Evolutionary biology; Phylogeography; Nestedness; Geography; Taxon; Phylogenetics; Genetic variation; Population; Genetics; Demography","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":[0.000245895,0.0001348466,0.0002296748,0.0009969367,0.0002275095,0.0003368399,0.0001566685,0.0002251551,0.0006305249],"category_scores_gemma":[0.0005946233,0.0001158758,0.0001501861,0.0004918759,0.0005506082,0.0002411514,0.000475646,0.0001522039,0.0001031082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002101777,"about_ca_system_score_gemma":0.00009187755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002880689,"about_ca_topic_score_gemma":0.006723668,"domain_scores_codex":[0.999858,0.00003641949,0.000008047722,0.00004949636,0.00001791701,0.00003000789],"domain_scores_gemma":[0.9996742,0.00006773815,0.0001257028,0.00003544468,0.00003988529,0.00005698709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001294798,0.0000200994,0.9677968,0.00003360756,0.0001021404,0.0001613409,0.001175693,0.0002218564,0.02234618,0.00008822517,0.00007350338,0.007851087],"study_design_scores_gemma":[7.653632e-7,0.000009204521,0.9995548,0.00000172977,0.000006229142,0.00003151835,0.0001949788,0.00007994446,0.00006101426,0.00001693153,0.00004161212,0.000001230857],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997088,0.00005967347,0.00005697776,0.000004505066,4.569441e-7,0.000001104257,0.00002332616,0.000002243836,0.000142806],"genre_scores_gemma":[0.9997405,0.00004371293,0.0001069784,0.000005096296,0.000002012523,0.00000154274,0.00005950953,0.000001628542,0.00003898181],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002880689,"threshold_uncertainty_score":0.005727828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09269371316045483,"score_gpt":0.2112143523036182,"score_spread":0.1185206391431634,"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."}}