{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000330286,0.0001647926,0.0005132669,0.0002586861,0.00005454336,0.00005260018,0.0001995714,0.00008626231,0.00037256],"category_scores_gemma":[0.00004629518,0.0001471354,0.0001258775,0.00008839105,0.0001140069,0.0002109988,0.0001377531,0.0001304771,0.000240299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007545143,"about_ca_system_score_gemma":0.00001511691,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01000906,"about_ca_topic_score_gemma":0.0007035832,"domain_scores_codex":[0.9984581,0.00001819376,0.0008084326,0.0003608435,0.00003721813,0.0003172257],"domain_scores_gemma":[0.999337,0.00004717784,0.0002057232,0.0002973395,0.00002376877,0.0000889804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004078765,0.0001300164,0.9665121,0.0001480894,0.0001143028,0.000007559227,0.002340293,0.0005920787,0.001090795,0.02383305,0.000625625,0.004565311],"study_design_scores_gemma":[0.0008040087,0.0001499767,0.9211995,0.0001129826,0.00001497074,0.000003760551,0.0007166446,0.01344592,0.001558151,0.05795242,0.003456171,0.0005854899],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930677,0.001250501,0.00006360015,0.001306556,0.00009304591,0.0002252244,0.00001297738,0.00001572127,0.003964713],"genre_scores_gemma":[0.9976586,0.0001918393,0.0002016113,0.00009871539,0.00005823635,0.00002354281,0.00001187076,0.00001800805,0.001737614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04531258,"threshold_uncertainty_score":0.9965834,"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."}}