{"id":"W2127196574","doi":"10.1111/jvs.12190","title":"Which is a better predictor of plant traits: temperature or precipitation?","year":2014,"lang":"en","type":"article","venue":"Journal of Vegetation Science","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":475,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Queen's University","funders":"European Regional Development Fund; Comisión Nacional de Investigación Científica y Tecnológica; Australian Research Council; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Conselho Nacional de Desenvolvimento Científico e Tecnológico; European Commission; University of Wisconsin-Eau Claire; Division of Environmental Biology; Department for Environment, Food and Rural Affairs, UK Government; National Science Foundation","keywords":"Precipitation; Mean radiant temperature; Vegetation (pathology); Ecology; Environmental science; Climatology; Physical geography; Climate change; Biology; Geography; Meteorology","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":[0.006901867,0.001008586,0.002804193,0.001511598,0.0005220076,0.001733155,0.001120381,0.001625117,0.002603775],"category_scores_gemma":[0.008007485,0.0005629022,0.005545321,0.002454075,0.0009950352,0.001370369,0.0006247408,0.001115822,0.0002814673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002838652,"about_ca_system_score_gemma":0.0005275355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003831914,"about_ca_topic_score_gemma":0.003075588,"domain_scores_codex":[0.9954932,0.002202833,0.0003243523,0.001690406,0.0001603922,0.0001287604],"domain_scores_gemma":[0.9850252,0.01069199,0.001857556,0.001467163,0.0005330945,0.0004249567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001037979,0.0000726947,0.86091,0.002680169,0.1063813,0.0002575261,0.0003102343,0.001699021,0.00378837,0.0004695778,0.00150075,0.02089244],"study_design_scores_gemma":[0.0002713086,0.0006105664,0.9035127,0.000758202,0.07517269,0.0005165644,0.0004315139,0.01048312,0.002430219,0.003151054,0.002546941,0.0001151698],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8936968,0.08510463,0.01132014,0.005013126,0.000703792,0.00003834164,0.002492609,0.0001798059,0.001450817],"genre_scores_gemma":[0.9955546,0.002264061,0.001024104,0.0004475357,0.0001793408,0.000009141851,0.0003154399,0.00004116944,0.0001646148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006901867,"threshold_uncertainty_score":0.03650099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008970263001350167,"score_gpt":0.24986261724323,"score_spread":0.2408923542418798,"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."}}