{"id":"W2766884206","doi":"10.1073/pnas.1708984114","title":"Mapping local and global variability in plant trait distributions","year":2017,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":294,"is_retracted":false,"has_abstract":true,"ca_institutions":"Algoma University","funders":"Wageningen University and Research; U.S. Department of Energy; Institute on the Environment, University of Minnesota; Office of Science; Research Councils UK; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Biological and Environmental Research; National Natural Science Foundation of China; Sight Research UK; Chinese Academy of Sciences; Australian Research Council; Centre of Excellence for Environmental Decisions, Australian Research Council; Natural Environment Research Council; University of Minnesota","keywords":"Trait; Bayesian probability; Grid; Range (aeronautics); Specific leaf area; Environmental science; Ecology; Biology; Computer science; Statistics; Geography; Mathematics; Photosynthesis; Botany","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":[0.0006263831,0.0002190581,0.0002081252,0.001013748,0.0001441698,0.0004919929,0.000189341,0.000201854,0.0005097261],"category_scores_gemma":[0.001177878,0.0001293653,0.0003471551,0.00118647,0.0002331223,0.0005367047,0.0004931265,0.0002714583,0.0001321267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002577067,"about_ca_system_score_gemma":0.0001759295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005197345,"about_ca_topic_score_gemma":0.007812032,"domain_scores_codex":[0.9997965,0.00003647752,0.000005450959,0.0001200292,0.00002351276,0.00001805239],"domain_scores_gemma":[0.9994037,0.0003183534,0.0001069196,0.0001062554,0.00004004351,0.00002483294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001577388,0.0001161814,0.6055657,0.0001211455,0.0003679123,0.0001729774,0.0005905603,0.14382,0.05229893,0.003289017,0.001468442,0.1920314],"study_design_scores_gemma":[0.00001593793,0.0000594816,0.8164734,0.00001396215,0.00008400259,0.0001392138,0.0002930099,0.1707208,0.004005938,0.005710169,0.002441875,0.00004229607],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9646295,0.0001704746,0.0316116,0.00006191894,0.000003586292,0.000008712035,0.001442287,0.0002535021,0.001818493],"genre_scores_gemma":[0.9874336,0.00008481504,0.01082712,0.00001630814,0.000004166525,0.00001251925,0.001402735,0.00004085924,0.0001778151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005197345,"threshold_uncertainty_score":0.01033419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02425264218788136,"score_gpt":0.2689974037144816,"score_spread":0.2447447615266002,"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."}}