{"id":"W4385324722","doi":"10.1016/j.agrformet.2023.109619","title":"Sources of uncertainty in simulating crop N2O emissions under contrasting environmental conditions","year":2023,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Soil Carbon and Nitrogen Dynamics","field":"Agricultural and Biological Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"EIT Climate-KIC; Agriculture and Agri-Food Canada; Analyses et Expérimentations pour les Ecosystèmes; Universidade Federal de Santa Maria; Indian Agricultural Research Institute; Queensland University of Technology; Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement; Agence Nationale de la Recherche","keywords":"Environmental science; Nitrification; Agroecosystem; Simulation modeling; Greenhouse gas; Spatial variability; Atmospheric sciences; Nitrogen cycle; Soil science; Nitrogen; Ecology; Mathematics; Chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001124688,0.0006729561,0.0005136624,0.0004519726,0.0004194987,0.0007668414,0.0008154775,0.001116816,0.0004721271],"category_scores_gemma":[0.002827965,0.0004127582,0.0008884731,0.0005775284,0.0004947603,0.0007884316,0.000525236,0.0009352593,0.00005013717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001252818,"about_ca_system_score_gemma":0.0007046463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03453239,"about_ca_topic_score_gemma":0.01864035,"domain_scores_codex":[0.9996339,0.0001250264,0.00003156196,0.00009352071,0.00005551397,0.00006047067],"domain_scores_gemma":[0.9984546,0.001136707,0.0001276003,0.00009165287,0.0001383728,0.00005100195],"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":[0.00004070333,0.00003592211,0.004863885,0.00001977719,0.00002995525,0.00002957785,0.00002397566,0.9928986,0.001045663,0.0001999884,0.0000273488,0.0007846144],"study_design_scores_gemma":[0.00001796289,0.00003208195,0.002906182,0.000006359205,0.00001695069,0.000007609236,0.00003690222,0.9951049,0.001416679,0.000313127,0.0001292869,0.00001203323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930961,0.0001157504,0.005177705,0.00008764935,0.00001229883,0.00001884221,0.0004471355,0.00006585073,0.0009786458],"genre_scores_gemma":[0.9976819,0.00004979461,0.001864553,0.00001836963,0.00000270978,0.00001699544,0.000247947,0.00001390254,0.0001038581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03453239,"threshold_uncertainty_score":0.06866282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01510528983248929,"score_gpt":0.223929151189276,"score_spread":0.2088238613567867,"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."}}