{"id":"W1981157906","doi":"10.1007/s11434-013-0016-5","title":"Applying a dual optimization method to quantify carbon fluxes: recent progress in carbon flux inversion","year":2013,"lang":"en","type":"article","venue":"Chinese Science Bulletin","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"University of Toronto; Ministry of Science and Technology of the People's Republic of China","keywords":"Flux (metallurgy); Inversion (geology); Scaling; Carbon flux; Mean squared error; Bayesian probability; Environmental science; Dual (grammatical number); Statistics; Computer science; Mathematics; Biological system; Atmospheric sciences; Ecosystem; Chemistry; Physics; Geology; Ecology; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008330493,0.000269804,0.0002267233,0.00005401996,0.0001732887,0.00009440554,0.0005037659,0.00009045202,0.001611005],"category_scores_gemma":[0.00011278,0.0002229273,0.00003631426,0.001595332,0.0005450929,0.00019615,0.0007038905,0.0001937679,0.0003861768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006746608,"about_ca_system_score_gemma":0.00002456776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001567232,"about_ca_topic_score_gemma":0.00002573415,"domain_scores_codex":[0.9973865,0.00008862821,0.0003309441,0.0007989574,0.0007754593,0.0006195393],"domain_scores_gemma":[0.9991642,0.00004329261,0.000101394,0.0003703494,0.00001418008,0.0003065452],"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":[0.0000221265,0.0001649226,0.3070411,0.000008837018,0.000002301997,0.00001014789,0.001003785,0.6374447,0.009637331,0.000007681789,0.0003175142,0.0443395],"study_design_scores_gemma":[0.0004238299,0.0001185919,0.1204874,0.00003306656,0.000005953485,0.00001122691,0.0005526341,0.8730244,0.0004425681,0.00008066923,0.004354069,0.0004655212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984271,0.00003752338,0.005580273,0.001759086,0.000201795,0.001277036,4.691921e-7,0.00006579433,0.006806975],"genre_scores_gemma":[0.7708432,0.00002036827,0.2274276,0.0005942844,0.00003334312,0.0003792392,0.000004397146,0.00002775807,0.0006697814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2355797,"threshold_uncertainty_score":0.9993017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00696791213906945,"score_gpt":0.242965838971446,"score_spread":0.2359979268323766,"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."}}