{"id":"W2473330229","doi":"10.1007/s10533-016-0219-3","title":"Carbon and energy fluxes in cropland ecosystems: a model-data comparison","year":2016,"lang":"en","type":"article","venue":"Biogeochemistry","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; Natural Resources Canada; University of Toronto; Environment and Climate Change Canada; McMaster University","funders":"Oak Ridge National Laboratory; U.S. Department of Energy; Biological and Environmental Research; National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Eddy covariance; Environmental science; Ecosystem; Atmospheric sciences; Biometeorology; Ecosystem model; Flux (metallurgy); Ecology; Canopy; Chemistry; Biology","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":[0.003117319,0.0004866818,0.0005594002,0.0006911004,0.0003202109,0.0009238402,0.0009173148,0.001062549,0.001841317],"category_scores_gemma":[0.006709969,0.0002378955,0.001191842,0.0009512419,0.0004068259,0.001634657,0.0003358496,0.0004265682,0.0002936423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001577477,"about_ca_system_score_gemma":0.000890743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0269248,"about_ca_topic_score_gemma":0.02143495,"domain_scores_codex":[0.9994408,0.0003104803,0.00005015716,0.00009942225,0.00005405664,0.00004493513],"domain_scores_gemma":[0.9935244,0.005074261,0.0002695681,0.0005922014,0.0004463868,0.00009313744],"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":[0.001643381,0.0003967446,0.04795203,0.0002305031,0.0004543468,0.0001039199,0.00007562256,0.9328155,0.002606095,0.002417787,0.002219753,0.009084336],"study_design_scores_gemma":[0.000531682,0.0003106691,0.05099116,0.00004799961,0.000298355,0.0001004962,0.0001929397,0.9373298,0.00445792,0.003666345,0.001989047,0.00008352794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921068,0.000185441,0.002144386,0.0003061309,0.00001855051,0.00001962784,0.003501224,0.0001537868,0.001563938],"genre_scores_gemma":[0.9960173,0.00007016167,0.001261167,0.00004161968,0.000004966061,0.0000219232,0.002319168,0.00004756916,0.0002160378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0269248,"threshold_uncertainty_score":0.05353612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01264684383222272,"score_gpt":0.2083984853091142,"score_spread":0.1957516414768915,"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."}}