{"id":"W4283034878","doi":"10.1111/1365-2435.14109","title":"Tree species identity drives nutrient use efficiency in young mixed‐species plantations, at both high and low water availability","year":2022,"lang":"en","type":"article","venue":"Functional Ecology","topic":"Forest ecology and management","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Forest Research Institute; Ministry of Natural Resources and Forestry; Université Laval; Centre de Géomatique du Québec","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Bordeaux; Agence Nationale de la Recherche; Biodiversa+","keywords":"Monoculture; Species richness; Nutrient; Dominance (genetics); Productivity; Biology; Ecology; Species diversity; Temperate climate; Plant litter; Context (archaeology); Biomass (ecology); Agronomy; Environmental science","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":[0.0003115958,0.0001943121,0.000282733,0.0003333353,0.0003674678,0.0006389436,0.0002343174,0.0001557435,0.0006970334],"category_scores_gemma":[0.0003749825,0.0002114331,0.0001360073,0.0001800182,0.0004191541,0.000228007,0.0003211342,0.0001929758,0.0001077335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028385,"about_ca_system_score_gemma":0.0003522317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04248176,"about_ca_topic_score_gemma":0.154949,"domain_scores_codex":[0.9998274,0.00003424003,0.000008371378,0.00005998897,0.00003053561,0.00003951398],"domain_scores_gemma":[0.9994279,0.0001420966,0.0001182658,0.00003389897,0.00006535793,0.0002124612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007685604,0.0001998665,0.7217064,0.00003971378,0.0000979311,0.0001065278,0.0007085151,0.000310432,0.2726233,0.00008347336,0.00009237422,0.003262853],"study_design_scores_gemma":[0.000002500362,0.00003517895,0.9986273,0.000001035513,0.000005108411,0.00001863113,0.0001086877,0.0002891374,0.0008397311,0.000009154967,0.00006161381,0.000001901588],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9998659,0.00001690187,0.00002748262,0.000001278184,2.89892e-7,0.000001313621,0.00002184479,0.000001164537,0.00006393509],"genre_scores_gemma":[0.9996566,0.00001371012,0.000093846,0.000005578002,5.438812e-7,0.000003711518,0.00008146005,0.000002489399,0.0001420328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04248176,"threshold_uncertainty_score":0.08446896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008790485961430209,"score_gpt":0.1842289436831204,"score_spread":0.1754384577216902,"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."}}