{"id":"W2030876073","doi":"10.1029/2005jg000045","title":"Simulating terrestrial carbon fluxes using the new biosphere model “biosphere model integrating eco‐physiological and mechanistic approaches using satellite data” (BEAMS)","year":2005,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Primary production; Biosphere; Environmental science; Biosphere model; Atmospheric sciences; Precipitation; Satellite; Climatology; Ecosystem; Carbon cycle; Flux (metallurgy); Climate model; Carbon flux; Climate change; Meteorology; Ecology; Geology; Physics; Chemistry; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004402144,0.0005169755,0.0004240418,0.0003105794,0.000340163,0.0005869265,0.0008123628,0.0007273045,0.001030577],"category_scores_gemma":[0.0007374437,0.0004148378,0.0007425942,0.0003707263,0.0003564638,0.001061714,0.0005328595,0.0004835197,0.0001666738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006815701,"about_ca_system_score_gemma":0.0009237838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01994631,"about_ca_topic_score_gemma":0.01655262,"domain_scores_codex":[0.9998833,0.00004104427,0.000007148142,0.00002583239,0.00002922451,0.00001344967],"domain_scores_gemma":[0.9997283,0.0001396869,0.00002536077,0.00003259551,0.00004395845,0.00003003773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001918164,0.00001409392,0.002618235,0.0000190839,0.0000459863,0.00002524276,0.00002227435,0.9908047,0.0009437212,0.00190834,0.0004084458,0.003170679],"study_design_scores_gemma":[0.00001910503,0.000007483527,0.0006943288,0.000002649485,0.00001023086,0.000007936493,0.000005417878,0.9967557,0.0003096257,0.001240517,0.000940502,0.000006470489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6847128,0.0006922238,0.2940522,0.001206593,0.0002226882,0.0001230854,0.00325918,0.001523397,0.01420779],"genre_scores_gemma":[0.9005414,0.0005342356,0.09362049,0.000178232,0.00007532187,0.0003593131,0.001803707,0.0002280472,0.002659218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01994631,"threshold_uncertainty_score":0.03966039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1432788444827665,"score_gpt":0.3305937304008213,"score_spread":0.1873148859180548,"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."}}