{"id":"W2908148979","doi":"10.1029/2018ms001363","title":"Modeling Global Riverine DOC Flux Dynamics From 1951 to 2015","year":2019,"lang":"en","type":"article","venue":"Journal of Advances in Modeling Earth Systems","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Environmental science; Dissolved organic carbon; Carbon cycle; Ecosystem; Flux (metallurgy); Terrestrial ecosystem; Global change; Water cycle; Hydrology (agriculture); Primary production; Atmospheric sciences; Climate change; Ecology; Oceanography; Geology; Chemistry; 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":[],"consensus_categories":[],"category_scores_codex":[0.0006882887,0.0002223893,0.0006026532,0.0001724731,0.00005148881,0.00012352,0.0004412446,0.00009093436,0.0001814229],"category_scores_gemma":[0.00003926635,0.0001835703,0.000119868,0.000326767,0.000009882642,0.000957434,0.00003913423,0.0002852684,0.0002074031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000408792,"about_ca_system_score_gemma":0.0001046537,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009852777,"about_ca_topic_score_gemma":0.0174644,"domain_scores_codex":[0.9975327,0.0001025774,0.0010444,0.0002891141,0.0006423297,0.0003888577],"domain_scores_gemma":[0.9989441,0.00005849938,0.0002853807,0.0002633099,0.0002115425,0.0002371512],"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":[0.0001200571,0.00001125472,0.09936429,0.00005195905,0.00001605891,0.00002885163,0.00005653036,0.8922573,0.000007645384,0.00004361707,0.00001618618,0.008026209],"study_design_scores_gemma":[0.0005095053,0.0002205323,0.0007988329,0.0004094546,0.00001031668,0.00007477391,0.0006558295,0.9944484,0.000001133551,0.0005370128,0.002116547,0.0002176568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9133588,0.00618017,0.07232022,0.00006342719,0.003349889,0.0002610292,0.0001361783,0.00001928987,0.004310959],"genre_scores_gemma":[0.9955878,0.0003489379,0.003235504,0.00005301342,0.0005145582,7.260219e-7,0.00003274382,0.000007375418,0.0002194028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1021911,"threshold_uncertainty_score":0.9967407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008240813638980377,"score_gpt":0.232382416596286,"score_spread":0.2241416029573056,"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."}}