{"id":"W4402421229","doi":"10.1002/ecy.4389","title":"Root and biomass allocation traits predict changes in plant species and communities over four decades of global change","year":2024,"lang":"en","type":"article","venue":"Ecology","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Université de Sherbrooke","funders":"H2020 European Research Council; Fonds Québécois de la Recherche sur la Nature et les Technologies; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Ecology; Understory; Biology; Plant community; Climate change; Trait; Biodiversity; Environmental change; Global change; Population; Abundance (ecology); Global warming; Beta diversity; Species richness; Canopy; Demography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0003425912,0.0001856568,0.0001401121,0.0005281513,0.0003836948,0.0004116091,0.0002066793,0.0003287153,0.001176795],"category_scores_gemma":[0.0009855214,0.00009281539,0.0001934982,0.000634276,0.0002867223,0.0002788172,0.0002717411,0.0003145939,0.0001963743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00102219,"about_ca_system_score_gemma":0.0005149706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3385574,"about_ca_topic_score_gemma":0.5899759,"domain_scores_codex":[0.999854,0.00001660977,0.000008042924,0.0000545259,0.00002709364,0.00003980354],"domain_scores_gemma":[0.9990545,0.0001396116,0.0003201071,0.00005820089,0.0002664793,0.0001610208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001614371,0.000005203941,0.9966008,0.000005090407,0.00002497639,0.000009640439,0.00008529769,0.0001707965,0.0009549275,0.000009878868,0.00008520215,0.002032124],"study_design_scores_gemma":[2.696279e-7,0.000002254929,0.9996604,9.625169e-7,0.000002484276,0.000006035636,0.00005647682,0.0001747727,0.00002838411,0.00000565558,0.00006149596,8.683259e-7],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986598,0.0001260409,0.0001401157,0.00003356793,0.000001799333,0.000002158594,0.0005994245,0.000006426661,0.0004306354],"genre_scores_gemma":[0.9991523,0.00004302639,0.0001439693,0.00001451062,0.000001863935,0.000002121411,0.0004259844,0.000002159367,0.0002140571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3385574,"threshold_uncertainty_score":0.6731735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0257258098140274,"score_gpt":0.2480248558630647,"score_spread":0.2222990460490373,"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."}}