{"id":"W2626221200","doi":"10.1038/s41559-017-0186","title":"Global hotspots and correlates of alien species richness across taxonomic groups","year":2017,"lang":"en","type":"article","venue":"Nature Ecology & Evolution","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":471,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Fundação para a Ciência e a Tecnologia; Ministerio de Economía y Competitividad; Volkswagen Foundation; Deutsche Forschungsgemeinschaft; Akademie Věd České Republiky; Austrian Science Fund; Grantová Agentura České Republiky; Ministério da Ciência, Tecnologia e Ensino Superior; European Commission","keywords":"Species richness; Ecology; Alien; Mainland; Taxonomic rank; Biology; Population; Geography; Introduced species; Mainland China; Taxon; China; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001790563,0.0001155974,0.0001702557,0.00001154852,0.0004616219,0.00003432104,0.0002798096,0.0003896038,0.003281782],"category_scores_gemma":[0.000134874,0.0001090834,0.0000452989,0.00007398683,0.0008021143,0.0002289302,0.0003458487,0.0001722304,0.0001791459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007281358,"about_ca_system_score_gemma":0.000007745631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001089809,"about_ca_topic_score_gemma":0.003580513,"domain_scores_codex":[0.9991648,0.00002762198,0.0001479206,0.0002640362,0.0001192511,0.0002763862],"domain_scores_gemma":[0.9993993,0.00002697786,0.0002196944,0.0002757538,0.00001815016,0.00006013698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003156797,0.00004459884,0.9881908,0.000004504726,0.000008540704,0.000001911998,0.0000452538,0.0000113158,0.0005875622,0.006190677,0.004745302,0.000137924],"study_design_scores_gemma":[0.0004078438,0.0000505356,0.9950995,0.000003596105,0.00001382651,0.00001826782,0.0004842303,0.00007656666,0.00009593728,0.000643472,0.003002067,0.0001041448],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851139,0.0001950544,0.000029656,0.0003898806,0.0007758347,0.0001416379,0.0001910781,0.00002480166,0.01313815],"genre_scores_gemma":[0.9994757,0.00006650308,0.00002661258,0.00005668173,0.0000408191,0.00001024344,0.0000255941,0.000004099946,0.00029374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0143618,"threshold_uncertainty_score":0.9976293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01056562323485341,"score_gpt":0.2597196562954088,"score_spread":0.2491540330605554,"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."}}