{"id":"W2368610762","doi":"","title":"DIETARY WATER FOOTPRINT OF URBAN AND RURAL RESIDENT IN JILIN PROVINCE","year":2013,"lang":"en","type":"article","venue":"Yunnan dili huanjing yanjiu","topic":"Regional Economic and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Footprint; Geography; Ecological footprint; Consumption (sociology); Rural area; Index (typography); Urbanization; Socioeconomics; Food consumption; Significant difference; Environmental protection; Agricultural economics; Ecology; Biology; Mathematics; Sustainability; Statistics; Medicine; Economics; Sociology","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":[0.0001527424,0.0001451111,0.0001077534,0.0006473436,0.0003409435,0.0002521449,0.0001405657,0.0001097135,0.001055897],"category_scores_gemma":[0.0003206386,0.0001162534,0.0001612291,0.001500625,0.000226039,0.0002612401,0.0002771219,0.0001667834,0.00006343847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008202439,"about_ca_system_score_gemma":0.0004592284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1283787,"about_ca_topic_score_gemma":0.2398057,"domain_scores_codex":[0.9999193,0.00001070122,0.000008315952,0.00002152966,0.00001532975,0.00002479836],"domain_scores_gemma":[0.9998078,0.00002048572,0.00008558677,0.00001227767,0.0000390338,0.0000348053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004589286,0.00001916282,0.9923113,0.00002773942,0.00002524491,0.0001769551,0.000965333,0.0001654379,0.0004764938,0.0001639712,0.0004980504,0.005124437],"study_design_scores_gemma":[7.48622e-7,0.000007212928,0.9986928,0.000004479318,0.000003869756,0.00002655388,0.0007350104,0.0001560792,0.00003105952,0.00001627591,0.0003237644,0.000002115856],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991084,0.00007253543,0.00002644038,0.0000493555,0.000001645629,0.000001890729,0.0002531175,0.000001440411,0.0004852097],"genre_scores_gemma":[0.999035,0.00009540687,0.00005944043,0.00001288381,0.000002050604,0.00000378784,0.0003693136,8.500023e-7,0.0004213447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1283787,"threshold_uncertainty_score":0.255263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01875534099897448,"score_gpt":0.1831536614913273,"score_spread":0.1643983204923528,"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."}}