{"id":"W3133459516","doi":"10.1111/ele.13704","title":"Climatic and evolutionary contexts are required to infer plant life history strategies from functional traits at a global scale","year":2021,"lang":"en","type":"article","venue":"Ecology Letters","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"FP7 Ideas: European Research Council; Max-Planck-Institut für demografische Forschung; Syddansk Universitet; Irish Research Council; British Ecological Society; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Ecological Society of America","keywords":"Trait; Ecology; Context (archaeology); Biology; Life history theory; Extinction (optical mineralogy); Climate change; Evolutionary ecology; Range (aeronautics); Global change; Life history; Adaptation (eye); Scale (ratio); Geography; Computer 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006405261,0.0001129297,0.0001760213,0.00001702787,0.0002095878,0.000007079222,0.0000615709,0.00008847025,0.002299799],"category_scores_gemma":[0.00007734842,0.0001188951,0.00003118499,0.00006565118,0.0003635783,0.0001386203,0.0001989475,0.00008076522,0.0004574739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008434626,"about_ca_system_score_gemma":0.00004718546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003971764,"about_ca_topic_score_gemma":0.01620274,"domain_scores_codex":[0.9990876,0.00009874048,0.0001743046,0.0003225972,0.00009931882,0.0002174613],"domain_scores_gemma":[0.9995769,0.0001641062,0.0000688666,0.00008314658,0.00001203101,0.00009490592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005128188,0.00005652527,0.8947932,0.000005476406,0.00006731562,0.00005996442,0.000461257,0.001744807,0.002056888,0.0002179069,0.1004529,0.00003256379],"study_design_scores_gemma":[0.0004035555,0.00003206637,0.9962451,0.000005413953,0.00002728279,0.00002509832,0.0002853589,0.0003055123,0.000003206749,0.0006587459,0.001886175,0.0001224901],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853261,0.0002426026,0.000210124,0.0119562,0.0004936663,0.0001047019,0.00007624654,0.0000292917,0.001561054],"genre_scores_gemma":[0.9802181,0.00001255594,0.0007345049,0.01855915,0.00004103654,0.00003753418,0.0000609321,0.000004930667,0.0003312448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1014519,"threshold_uncertainty_score":0.9986122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01302645715574159,"score_gpt":0.2024317081280791,"score_spread":0.1894052509723375,"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."}}