{"id":"W2580147412","doi":"10.1007/s10113-016-1101-5","title":"Nitrogen use efficiencies in Chinese agricultural systems and implications for food security and environmental protection","year":2017,"lang":"en","type":"article","venue":"Regional Environmental Change","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":104,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Biotechnology and Biological Sciences Research Council; National Natural Science Foundation of China","keywords":"Food security; Environmental science; Livestock; Agriculture; Grassland; Grazing; Productivity; Agricultural productivity; Agronomy; Reactive nitrogen; Agroforestry; Environmental protection; Natural resource economics; Nitrogen; Geography; Ecology; Biology; Economics; Forestry; Chemistry","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":[0.00009624314,0.0001971192,0.0001518927,0.00003132352,0.0005098448,0.0001085855,0.0001539852,0.00008843294,0.000004263524],"category_scores_gemma":[0.000009093432,0.0001583619,0.00004442379,0.00002758626,0.0004502235,0.000661381,0.0002808124,0.00008930411,0.000007781749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002224646,"about_ca_system_score_gemma":0.000001151336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002935372,"about_ca_topic_score_gemma":0.00007647133,"domain_scores_codex":[0.9989823,0.00002517592,0.0001584193,0.000412232,0.0001635153,0.0002583347],"domain_scores_gemma":[0.9994998,0.00003133521,0.000121882,0.0002342198,7.596591e-7,0.0001119804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002912932,0.0001258692,0.9969609,0.00001282242,0.000007808299,6.93176e-7,0.000579173,0.00002580049,0.001569903,0.00007358263,0.00001549198,0.0005988478],"study_design_scores_gemma":[0.0006582437,0.0001726372,0.9922631,0.00001263642,0.00001129786,0.00006958499,0.0003297068,0.003487982,0.00002995902,0.001882884,0.0008632184,0.0002186905],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976595,0.0002676168,0.00003043694,0.0004969896,0.00005486296,0.001105279,0.0003385795,0.00001570651,0.00003105013],"genre_scores_gemma":[0.9988419,0.0003071904,0.00004906696,0.00004751451,0.00007338428,0.0005184855,0.0001107006,0.00001462724,0.00003714453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00469772,"threshold_uncertainty_score":0.645781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03834764145368134,"score_gpt":0.2223184746093719,"score_spread":0.1839708331556906,"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."}}