{"id":"W2515588298","doi":"10.1038/ncomms12358","title":"Adaptation to elevated CO2 in different biodiversity contexts","year":2016,"lang":"en","type":"article","venue":"Nature Communications","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; University of British Columbia","funders":"Division of Environmental Biology; Natural Sciences and Engineering Research Council of Canada; University of Minnesota; U.S. Department of Energy; Western Sydney University; Hawkesbury Institute for the Environment, Western Sydney University; National Science Foundation","keywords":"Biodiversity; Species richness; Adaptation (eye); Local adaptation; Ecology; Biology; Genetic Fitness; Genetic diversity; Diversity (politics); Ecosystem diversity; Population; Biomass (ecology); Biological evolution; Demography","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":[0.0003363893,0.0002488702,0.0003124725,0.0004321309,0.0004534337,0.0005886974,0.000340387,0.0003393806,0.0006606354],"category_scores_gemma":[0.0005438752,0.0001426238,0.0002502679,0.0002504182,0.0005358806,0.0003612094,0.0009745159,0.0003896375,0.00006987457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006122742,"about_ca_system_score_gemma":0.0002433811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002858703,"about_ca_topic_score_gemma":0.007192351,"domain_scores_codex":[0.9996915,0.00007235771,0.00001975301,0.00008872685,0.00004613433,0.00008150236],"domain_scores_gemma":[0.9995503,0.0000766272,0.00009344166,0.00006383384,0.00005962339,0.0001562032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005033323,0.00007589026,0.05194941,0.00003839342,0.00008688308,0.000143523,0.0002646563,0.0008417488,0.9434993,0.0001796217,0.00002811828,0.002389123],"study_design_scores_gemma":[0.00002460102,0.0005018663,0.9650209,0.000006945651,0.00006150323,0.0003072931,0.0007169278,0.002516418,0.03005642,0.0003488578,0.0004064385,0.00003185659],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996346,0.00003429271,0.00008618644,0.000007185763,0.000001172425,0.000001970874,0.0000189342,0.00000290661,0.0002127512],"genre_scores_gemma":[0.9997239,0.00002187043,0.0001288577,0.00001153971,0.000001291779,0.000003725577,0.00004728065,0.000002594478,0.00005897239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002858703,"threshold_uncertainty_score":0.005684137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04027576077853513,"score_gpt":0.2764995608071362,"score_spread":0.2362238000286011,"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."}}