{"id":"W3128361715","doi":"10.1073/pnas.2019355118","title":"Functional rarity and evenness are key facets of biodiversity to boost multifunctionality","year":2021,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Ministerio de Ciencia e Innovación; Ministerio de Educación, Cultura y Deporte; Ministerio de Economía y Competitividad; British Ecological Society; Generalitat Valenciana; Ecological Society of America","keywords":"Species evenness; Biodiversity; Biome; Ecosystem; Abundance (ecology); Global biodiversity; Key (lock); Ecology; Ecosystem services; Species richness; Biology; Species diversity; Rank abundance curve; Environmental resource management; Environmental science","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":[0.001061112,0.0005479598,0.0005490183,0.001007633,0.000545523,0.0009852909,0.000338058,0.0003807421,0.001779257],"category_scores_gemma":[0.002081445,0.0002368325,0.0003823163,0.0003652127,0.0008755499,0.0009962773,0.001486094,0.0005335283,0.0002094954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004420315,"about_ca_system_score_gemma":0.0003557234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005594557,"about_ca_topic_score_gemma":0.002397014,"domain_scores_codex":[0.9994377,0.0002110185,0.00004104117,0.000130268,0.00008310351,0.00009680176],"domain_scores_gemma":[0.9971752,0.0006607322,0.0008870242,0.000413078,0.000217929,0.0006460336],"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":[0.0004586001,0.0001926894,0.2918338,0.0004463576,0.0004420828,0.0002221049,0.0004897368,0.003520117,0.655883,0.002945371,0.0001845619,0.04338171],"study_design_scores_gemma":[0.00001290527,0.0003909784,0.9720128,0.00003923625,0.0001301596,0.0003783534,0.0003738513,0.005394593,0.01385908,0.005777013,0.001595396,0.00003582368],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993946,0.0004653262,0.003997538,0.00008346046,0.000005538167,0.000009343014,0.00006550662,0.00002927601,0.001397861],"genre_scores_gemma":[0.9969994,0.00009435798,0.002607648,0.00003455989,0.000006127739,0.000006616522,0.00005285779,0.00001067712,0.0001877354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001779257,"threshold_uncertainty_score":0.005952239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07872698605495672,"score_gpt":0.2762237178920685,"score_spread":0.1974967318371117,"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."}}