{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004159703,0.0007887272,0.001096472,0.00324555,0.0004880395,0.0007245709,0.0008226489,0.0003708542,0.0003908238],"category_scores_gemma":[0.002285377,0.0002959407,0.0025826,0.004294351,0.0005436637,0.0007014384,0.0007636761,0.0002409354,0.00003400769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001907214,"about_ca_system_score_gemma":0.002301856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07004476,"about_ca_topic_score_gemma":0.04726441,"domain_scores_codex":[0.9989513,0.0002915358,0.0001592153,0.000326457,0.0001765607,0.00009489836],"domain_scores_gemma":[0.998705,0.0005464568,0.000332928,0.0001449811,0.0001603508,0.0001103005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004119976,0.00007592653,0.9428813,0.001676865,0.01348155,0.0004030004,0.0007154769,0.005439369,0.005149658,0.001035606,0.0003786145,0.02835066],"study_design_scores_gemma":[0.00004185395,0.0001198221,0.9845953,0.00008427151,0.00504673,0.00008617202,0.0004161681,0.006990323,0.0007210958,0.0006829063,0.001174927,0.00004044209],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881659,0.009849565,0.0007351289,0.0002262141,0.000008113478,0.00001303918,0.0006874055,0.00001243626,0.0003023173],"genre_scores_gemma":[0.9972183,0.001671048,0.0004532822,0.00003952846,0.00000657586,0.00001356835,0.0005252623,0.000004663481,0.00006772851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07004476,"threshold_uncertainty_score":0.1392741,"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."}}