{"id":"W4200348628","doi":"10.3389/ffgc.2021.704469","title":"Tradeoffs and Synergies in Tropical Forest Root Traits and Dynamics for Nutrient and Water Acquisition: Field and Modeling Advances","year":2021,"lang":"en","type":"article","venue":"Frontiers in Forests and Global Change","topic":"Plant nutrient uptake and metabolism","field":"Agricultural and Biological Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Biological and Environmental Research; Natural Environment Research Council; Office of Science; U.S. Department of Energy; California Institute of Technology; European Commission; Sight Research UK; National Aeronautics and Space Administration; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Leverhulme Trust; National Science Foundation; Royal Society; Jet Propulsion Laboratory; New Phytologist Foundation; Incyte","keywords":"Nutrient; Biomass (ecology); Ecosystem; Resource (disambiguation); Tropics; Nutrient cycle; Terrestrial ecosystem; Primary production; Tropical climate","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":[0.001838544,0.0007315846,0.001088286,0.000765463,0.0002369635,0.001022002,0.001415375,0.0008018306,0.001573567],"category_scores_gemma":[0.00251971,0.0003692396,0.001016595,0.001165865,0.0007091647,0.001255749,0.0005503726,0.0009219829,0.0001534468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007347489,"about_ca_system_score_gemma":0.0008981567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02864604,"about_ca_topic_score_gemma":0.02029412,"domain_scores_codex":[0.9997855,0.00007033,0.00001706973,0.00007993135,0.0000233316,0.00002391655],"domain_scores_gemma":[0.9974336,0.002082412,0.0001728419,0.00008292096,0.000170935,0.00005716503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003090001,0.000544977,0.07167466,0.003305322,0.0007941712,0.0002585464,0.0004221819,0.81815,0.005165566,0.02682523,0.006787554,0.06576271],"study_design_scores_gemma":[0.00005608247,0.0001397762,0.02259317,0.0004950582,0.0003864302,0.00007722643,0.0003565985,0.9430336,0.0009767096,0.01775619,0.01402193,0.0001073039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8307787,0.05871534,0.08481128,0.002042643,0.0001457058,0.00009739646,0.008597636,0.0003993215,0.01441213],"genre_scores_gemma":[0.9359366,0.02252286,0.03666307,0.000327366,0.0001426025,0.000145232,0.003114607,0.00008359028,0.001064091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02864604,"threshold_uncertainty_score":0.05695862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0183871891140302,"score_gpt":0.223157203619884,"score_spread":0.2047700145058538,"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."}}