{"id":"W1811445455","doi":"10.1371/journal.pone.0140767","title":"Yield Gap, Indigenous Nutrient Supply and Nutrient Use Efficiency for Maize in China","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Crop Yield and Soil Fertility","field":"Agricultural and Biological Sciences","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"Plant Biotechnology Institute","funders":"National Key Research and Development Program of China; Chinese Academy of Agricultural Sciences; National Natural Science Foundation of China; International Plant Nutrition Institute","keywords":"Yield gap; Nutrient; Nutrient management; Food security; Yield (engineering); Agriculture; Agronomy; Phosphorus; Environmental science; Agricultural soil science; Fertilizer; Crop yield; Agricultural engineering; Agricultural science; Mathematics; Soil fertility; Biology; Soil water; Ecology; Chemistry; Engineering; Physics; Soil science","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":[0.0002593794,0.000109989,0.0001994395,0.00001407962,0.0001041202,0.00007113524,0.000129133,0.00008391067,0.00003807305],"category_scores_gemma":[0.0002901503,0.00004560974,0.00003929167,0.0001783009,0.00004605902,0.00009797962,0.00006268686,0.00009776049,0.000006692921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003541229,"about_ca_system_score_gemma":0.00001331227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001004263,"about_ca_topic_score_gemma":0.001042061,"domain_scores_codex":[0.9989891,0.00002830067,0.0001914422,0.0002744756,0.0002143265,0.0003023714],"domain_scores_gemma":[0.9994749,0.0002026236,0.00004082991,0.00006397007,0.00005909876,0.000158595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001038898,0.01147365,0.8003919,0.000125571,0.00005094168,0.00002149851,0.007865672,0.00000375112,0.1604061,0.001033241,0.0006869343,0.01690187],"study_design_scores_gemma":[0.0009151886,0.001585969,0.9578608,0.0001585579,0.0000357095,0.000002932917,0.0007840388,0.0002052411,0.03188916,0.004093927,0.002083386,0.0003850868],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980211,0.0003080065,0.000006187145,0.0007467106,0.0000390233,0.0006217012,0.00005819033,0.00003647235,0.0001625838],"genre_scores_gemma":[0.999277,0.0000810426,0.000126093,0.0001188173,0.00009202233,0.0000498507,0.00002409885,8.89186e-7,0.0002301846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.157469,"threshold_uncertainty_score":0.1859911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09632506567099133,"score_gpt":0.2242147595390499,"score_spread":0.1278896938680586,"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."}}