{"id":"W4323928229","doi":"10.1016/j.agrformet.2023.109412","title":"Environmental controls on carbon fluxes in an urban forest in the Megalopolis of Beijing, 2012-2020","year":2023,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Eddy covariance; Environmental science; Ecosystem respiration; Beijing; Carbon sink; Ecosystem; Biometeorology; Afforestation; Hydrometeorology; Atmospheric sciences; Ecology; Geography; Canopy; Precipitation; China; Agroforestry; Meteorology","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":[0.0001329887,0.0002075484,0.0002028591,0.0005225945,0.0003939455,0.0009219179,0.0003048036,0.000308128,0.001489348],"category_scores_gemma":[0.0002197456,0.0001601468,0.0002729448,0.001187232,0.0003654494,0.0003670694,0.000548783,0.0002228505,0.0001353383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002579338,"about_ca_system_score_gemma":0.001017534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1227817,"about_ca_topic_score_gemma":0.2794982,"domain_scores_codex":[0.9998953,0.00001365534,0.000008705048,0.00002217695,0.00001566186,0.00004447874],"domain_scores_gemma":[0.9998416,0.00001478833,0.0000422032,0.000009219736,0.00003640476,0.00005585883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003157309,0.0001282419,0.9754863,0.00005149798,0.0002152425,0.001312594,0.0005636489,0.007503406,0.00368371,0.001827866,0.0021056,0.006806131],"study_design_scores_gemma":[0.000005347542,0.00001201114,0.9940712,0.000003516629,0.00002603597,0.00004533609,0.0005360829,0.004163876,0.0002291324,0.0001095231,0.0007901015,0.000007895562],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982987,0.00006995809,0.00005356441,0.000149666,0.000004569878,0.000003610193,0.0006484122,0.000008107116,0.000763462],"genre_scores_gemma":[0.9992411,0.0000406384,0.00002403357,0.000009601275,0.00000291934,0.000002466491,0.0002977467,0.000001523435,0.000379935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1227817,"threshold_uncertainty_score":0.244134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005573122438023105,"score_gpt":0.1883959636448169,"score_spread":0.1828228412067938,"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."}}