{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000369352,0.0002988625,0.0002463092,0.0003132377,0.0003234786,0.00006045761,0.0007477795,0.0001924536,0.001770159],"category_scores_gemma":[0.0000400635,0.0002756503,0.00004707251,0.0009772826,0.005304673,0.0006273197,0.001830099,0.0002567839,0.0002250489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001221092,"about_ca_system_score_gemma":0.0000142106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007753113,"about_ca_topic_score_gemma":0.000196866,"domain_scores_codex":[0.9973168,0.00003920165,0.0003321824,0.0008719995,0.000482774,0.0009570141],"domain_scores_gemma":[0.9990265,0.000016225,0.00008807094,0.0005892783,0.000001024405,0.0002788932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004368156,0.000233316,0.8191652,0.000002059995,0.000003528781,0.000006845191,0.0002076518,0.0000602057,0.1306722,0.0006562387,0.00002082908,0.04896754],"study_design_scores_gemma":[0.0004681685,0.0005124052,0.8929344,0.000004931052,0.00000560072,0.00006079717,0.00122562,0.0003918595,0.07036289,0.0313047,0.002313203,0.0004153722],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918091,0.0001295762,0.0001067992,0.0009550506,0.00004857497,0.0004223234,0.000004213069,0.00008378337,0.006440608],"genre_scores_gemma":[0.9983081,0.00007995373,0.001087032,0.0001603924,0.000009590035,0.0001388571,0.0000022104,0.00001980793,0.0001940909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07376925,"threshold_uncertainty_score":0.9999695,"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."}}