{"id":"W2790947302","doi":"10.3390/su10051465","title":"The Impact of Proximity to Wet Markets and Supermarkets on Household Dietary Diversity in Nanjing City, China","year":2018,"lang":"en","type":"article","venue":"Sustainability","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Balsillie School of International Affairs","funders":"Social Sciences and Humanities Research Council of Canada; International Development Research Centre","keywords":"Diversity (politics); China; Food market; Business; Agricultural economics; Household income; Geography; Healthy food; Poisson regression; Survey data collection; Economics; Environmental health; Agriculture; Food science; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000973255,0.0001307089,0.0002568584,0.00005623698,0.0004160122,0.00001048509,0.00009439627,0.00005121955,0.00002141347],"category_scores_gemma":[0.001659875,0.00008284023,0.00010324,0.0002227015,0.0003606422,0.00006880386,0.0005691001,0.0001554361,4.727779e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005982346,"about_ca_system_score_gemma":0.000113662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002267914,"about_ca_topic_score_gemma":0.0002466328,"domain_scores_codex":[0.9988979,0.0001095286,0.0001868144,0.0002666436,0.0002270266,0.0003120744],"domain_scores_gemma":[0.9989696,0.0002511562,0.00003610817,0.0003021572,0.0003089327,0.0001321185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003351037,0.000402211,0.9908062,0.0001569197,0.00003713452,0.00001455284,0.0005375044,0.000001430492,0.0000206801,0.00009229632,0.002350457,0.002229615],"study_design_scores_gemma":[0.0008069319,0.001039035,0.9905985,0.00004480128,0.00001406264,0.000003764362,0.0005463735,0.0000220846,0.00005079707,0.006597844,0.0001950816,0.00008072856],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934541,0.0001052107,0.000001786091,0.003988791,0.00003878403,0.0008242257,0.00003715097,0.00001754036,0.001532439],"genre_scores_gemma":[0.9996778,0.00005115087,0.00002490987,0.00008986714,0.00006822048,0.00001136566,0.000002739148,0.000006075593,0.00006785292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006505548,"threshold_uncertainty_score":0.3428423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02477543068038832,"score_gpt":0.3053739764471162,"score_spread":0.2805985457667278,"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."}}