{"id":"W4311572563","doi":"10.1038/s41597-022-01774-9","title":"The global spectrum of plant form and function: enhanced species-level trait dataset","year":2022,"lang":"en","type":"article","venue":"Scientific Data","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":118,"is_retracted":false,"has_abstract":true,"ca_institutions":"Algoma University; University of Waterloo; University of Saskatchewan; Université de Sherbrooke","funders":"Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Russian Science Foundation; Natural Environment Research Council; Consejo Nacional de Investigaciones Científicas y Técnicas; Newton Fund; Fondo para la Investigación Científica y Tecnológica; Inter-American Institute for Global Change Research; Universidad Nacional de Córdoba","keywords":"Trait; Biology; Categorical variable; Vascular plant; Specific leaf area; Plant species; Taxonomic rank; Botany; Ecology; Statistics; Mathematics; Species richness; 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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009124335,0.00005960939,0.00007001706,0.00001150493,0.001427975,0.00004898662,0.0007590026,0.00001096502,0.001506374],"category_scores_gemma":[0.00004142928,0.00004476224,0.00001087132,0.0002591069,0.0007473572,0.00019688,0.002319697,0.00007082834,0.00004274119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005839016,"about_ca_system_score_gemma":0.00001728264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003872728,"about_ca_topic_score_gemma":0.00538628,"domain_scores_codex":[0.9990093,0.00003678493,0.0001446384,0.0003716059,0.0002664184,0.0001712974],"domain_scores_gemma":[0.9991344,0.00006117862,0.00007792571,0.0006931211,0.000002941833,0.00003047707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001014807,0.0001333056,0.01649864,0.000009826663,0.00005950499,0.000004227202,0.0004249699,0.0003375839,0.0007353289,0.01291919,0.9633108,0.00546509],"study_design_scores_gemma":[0.0002597067,0.00007273346,0.2557794,0.000001416539,0.00002382534,0.00001289991,0.0009462516,0.003260662,0.00005860985,0.008472409,0.7310078,0.0001042969],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2550001,0.0003608713,0.003486481,0.004106052,0.005747812,0.0007120076,0.7076818,0.0000454493,0.0228595],"genre_scores_gemma":[0.9750172,0.00002234716,0.000242058,0.0001117294,0.00002029019,0.00001640162,0.02038356,0.000003351653,0.004182992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7200172,"threshold_uncertainty_score":0.999872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03617426852601272,"score_gpt":0.2472785866775703,"score_spread":0.2111043181515576,"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."}}