{"id":"W2291421187","doi":"10.1038/srep22130","title":"A comprehensive estimate of recent carbon sinks in China using both top-down and bottom-up approaches","year":2016,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":128,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Oceanic and Atmospheric Administration; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; National Natural Science Foundation of China","keywords":"Top-down and bottom-up design; China; Carbon sink; Computer science; Geography; Biology; Ecology; Ecosystem; Archaeology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004263975,0.0001613776,0.0002179043,0.0000195566,0.00009825881,0.00003166843,0.0001021177,0.00007245844,0.0002213197],"category_scores_gemma":[0.00002534051,0.0001164624,0.00003595386,0.0002787975,0.0009938643,0.0001673132,0.0002757166,0.00008123127,0.000003341519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002264322,"about_ca_system_score_gemma":0.00001844892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005298933,"about_ca_topic_score_gemma":0.00003937002,"domain_scores_codex":[0.9981913,0.00004040325,0.0004071775,0.000660922,0.0003868092,0.0003134193],"domain_scores_gemma":[0.9991597,0.00001748755,0.0002447382,0.0004659698,0.000003893513,0.0001082076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002481006,0.000138659,0.8195871,0.00002565509,0.00001089264,0.0001329486,0.001045341,0.01778289,0.1198698,0.00001562212,0.00006949328,0.04129687],"study_design_scores_gemma":[0.0009934045,0.0001173436,0.8793598,0.000181753,0.00005259009,0.0005898167,0.0006580288,0.08957011,0.01442995,0.007729628,0.0054772,0.0008403487],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963357,0.00007651354,0.0009262208,0.00007555116,0.0007377103,0.0002322331,8.659654e-7,0.00001650503,0.001598721],"genre_scores_gemma":[0.9893036,0.00003107678,0.009863756,0.00000992173,0.000007390885,0.000005473409,0.000002096994,0.00001401429,0.0007627103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1054398,"threshold_uncertainty_score":0.4749198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02269887303064293,"score_gpt":0.2301114386772627,"score_spread":0.2074125656466198,"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."}}