{"id":"W2330063084","doi":"10.1021/es401344h","title":"Nitrogen Footprint in China: Food, Energy, and Nonfood Goods","year":2013,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":141,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Footprint; Per capita; Ecological footprint; Environmental science; Carbon footprint; Production (economics); Agricultural economics; Natural resource economics; Economics; Geography; Sustainability; Microeconomics; Biology; Ecology; Greenhouse gas; Environmental health; Population","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":[0.0006131332,0.0006140817,0.0002965627,0.001538537,0.0004864422,0.0005432405,0.0003704784,0.0003170602,0.0005898552],"category_scores_gemma":[0.0003379264,0.0001356388,0.0004400466,0.003553448,0.0003985247,0.001092051,0.0007491609,0.0001142394,0.00006222637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003221876,"about_ca_system_score_gemma":0.001965519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1262495,"about_ca_topic_score_gemma":0.2151898,"domain_scores_codex":[0.9997187,0.00003750647,0.00001632799,0.00005596554,0.0001336293,0.00003788733],"domain_scores_gemma":[0.9997628,0.00002457517,0.00007993556,0.00002685449,0.00008458964,0.00002117987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001859029,0.00005652436,0.8753682,0.0003110451,0.0002315946,0.0007451635,0.00071641,0.01850511,0.01120668,0.002223381,0.001053085,0.08939692],"study_design_scores_gemma":[0.000006334238,0.00006876567,0.97954,0.00003709021,0.0000676685,0.0001224769,0.0005268021,0.009757983,0.00323802,0.001235151,0.005376739,0.00002306934],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928325,0.001015506,0.00123991,0.0001930669,0.000008048705,0.00001827595,0.0006421031,0.00002509452,0.004025481],"genre_scores_gemma":[0.9955036,0.0008336038,0.001610289,0.00007449836,0.000004830319,0.00001777552,0.0007023643,0.000008453285,0.001244494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1262495,"threshold_uncertainty_score":0.2510293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002903438825560671,"score_gpt":0.1910314432522743,"score_spread":0.1881280044267136,"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."}}