{"id":"W2810571593","doi":"10.1016/j.agrformet.2018.06.015","title":"Terrestrial versus aquatic carbon fluxes in a subtropical agricultural floodplain over an annual cycle","year":2018,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Australian Research Council; Commonwealth Scientific and Industrial Research Organisation","keywords":"Environmental science; Carbon sink; Eddy covariance; Carbon cycle; Ecosystem; Floodplain; Wetland; Terrestrial ecosystem; Aquatic ecosystem; Hydrology (agriculture); Ecosystem respiration; Biogeochemistry; Sink (geography); Ecology; Biology; Geography; Geology","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":[0.00009365282,0.0002607656,0.0002665112,0.000009542972,0.000127888,0.00002404777,0.0002074086,0.000201453,0.0001912097],"category_scores_gemma":[0.00002775186,0.0001564299,0.0000536598,0.0001968078,0.0006300298,0.0003142738,0.0002187608,0.0001949653,0.00005595204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001319614,"about_ca_system_score_gemma":0.000003460802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004024928,"about_ca_topic_score_gemma":0.01600455,"domain_scores_codex":[0.998421,0.0001230806,0.0002685907,0.0004778327,0.0002058001,0.0005036764],"domain_scores_gemma":[0.9995323,0.00006063318,0.00007398473,0.0001430466,0.000002659788,0.0001873141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001083365,0.0004115219,0.9724399,0.000004870542,0.00006022652,0.00004348901,0.002335287,0.004751724,0.01017455,0.0004150144,0.0003749274,0.007905097],"study_design_scores_gemma":[0.001640308,0.001382986,0.9903537,0.000003143116,0.00003176532,0.00004031407,0.0006730798,0.004934845,0.00003815634,0.0003057406,0.0003235423,0.0002724322],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976959,0.00003017714,0.00001366165,0.0001853317,0.000386397,0.0002106837,0.000003304741,0.00003822953,0.001436319],"genre_scores_gemma":[0.9983079,0.00001751391,0.0009896669,0.00009466922,0.0003218421,0.00002247588,0.00003001427,0.000009359891,0.0002065431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01791376,"threshold_uncertainty_score":0.8930914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00573616541547612,"score_gpt":0.204050019726294,"score_spread":0.1983138543108179,"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."}}