{"id":"W2106811285","doi":"10.1111/j.1365-2486.2011.02562.x","title":"Terrestrial biosphere models need better representation of vegetation phenology: results from the <scp>N</scp>orth <scp>A</scp>merican <scp>C</scp>arbon <scp>P</scp>rogram <scp>S</scp>ite <scp>S</scp>ynthesis","year":2011,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":715,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Alberta; University of Toronto; Environment and Climate Change Canada; Queen's University; McMaster University","funders":"Northern Research Station; U.S. Forest Service; Office of Science; U.S. Department of Agriculture; U.S. Department of Energy; National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Phenology; Evergreen; Deciduous; Seasonality; Ecosystem; Vegetation (pathology); Ecology; Biosphere; Atmospheric sciences; Growing season; Environmental science; Climatology; Biology; Physics","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":[0.00138797,0.0008688395,0.0006496802,0.0003538538,0.000442805,0.001258299,0.0006982956,0.0007750508,0.00121168],"category_scores_gemma":[0.002926424,0.0004183639,0.0007267185,0.000495779,0.0003402332,0.000946962,0.0004755924,0.0005704005,0.0001984376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001493804,"about_ca_system_score_gemma":0.001197643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1576585,"about_ca_topic_score_gemma":0.1356775,"domain_scores_codex":[0.9997359,0.0001153397,0.0000154203,0.00006636217,0.00003293422,0.00003413625],"domain_scores_gemma":[0.9984494,0.0008330248,0.0001038861,0.0001794354,0.0002493771,0.0001848083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003741842,0.0001945672,0.1106893,0.00006571131,0.0002599709,0.00008125191,0.0001001861,0.8756753,0.003102959,0.0002290158,0.0009437253,0.008283913],"study_design_scores_gemma":[0.00009361807,0.00007419032,0.03191594,0.00001185933,0.00006050458,0.00001507555,0.00008484929,0.9658763,0.001145398,0.0002052164,0.0005000099,0.00001693387],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961415,0.000149214,0.001450211,0.0001599398,0.000008938413,0.00001665294,0.0005842466,0.0001611945,0.001328269],"genre_scores_gemma":[0.9969998,0.00006350449,0.001728606,0.00003008182,0.000005606702,0.00001461919,0.0008022084,0.00004509396,0.0003103816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1576585,"threshold_uncertainty_score":0.3134817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03879080836365131,"score_gpt":0.2397940026764399,"score_spread":0.2010031943127885,"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."}}