{"id":"W2578219231","doi":"10.5194/bg-14-145-2017","title":"Transient dynamics of terrestrial carbon storage: mathematical foundation and its applications","year":2017,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"University of Tennessee, Knoxville; U.S. Department of Homeland Security; National Institute for Mathematical and Biological Synthesis; U.S. Department of Agriculture; U.S. Department of Energy; National Science Foundation","keywords":"Environmental science; Residence time (fluid dynamics); Ecosystem; Terrestrial ecosystem; Sink (geography); Atmospheric sciences; Carbon sink; Carbon cycle; Soil science; Ecology; Engineering; Physics; Geography; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001646568,0.00008151114,0.00009813307,0.000005221217,0.0002941113,0.00004511953,0.0003051802,0.00004158284,0.0001097551],"category_scores_gemma":[0.00002643681,0.00006773436,0.00002453225,0.00005386258,0.0007947169,0.0001888133,0.0001328967,0.00003862133,0.00001498211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006304995,"about_ca_system_score_gemma":0.000006937141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000474674,"about_ca_topic_score_gemma":0.0001220116,"domain_scores_codex":[0.999234,0.00001209628,0.0001498928,0.0002213146,0.0002462839,0.0001364457],"domain_scores_gemma":[0.9995534,0.00002003296,0.0001367415,0.0002194511,0.000001644123,0.00006869625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009464244,0.001036903,0.6310894,0.0001039704,0.00003831571,0.000008498605,0.002068173,0.008293195,0.02412445,0.02512689,0.00003828815,0.3079772],"study_design_scores_gemma":[0.0003595781,0.0001353272,0.3336756,0.00001412303,0.00003160527,0.000008592609,0.0003608596,0.6589703,0.000285256,0.004686877,0.001228783,0.000243127],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805973,0.00001594662,0.01373915,0.0002403889,0.00008065496,0.0002234231,0.000004183865,0.0000107028,0.005088266],"genre_scores_gemma":[0.9955001,0.00004463938,0.004131607,0.000009995005,0.00001801255,0.00001841943,0.000002489778,0.000004192786,0.0002705435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6506771,"threshold_uncertainty_score":0.2928168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01325375978600729,"score_gpt":0.2389781692305264,"score_spread":0.2257244094445191,"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."}}