{"id":"W2213113255","doi":"10.1016/j.apgeog.2015.12.002","title":"The role of socio-economic status and spatial effects on fresh food access: Two case studies in Canada","year":2015,"lang":"en","type":"article","venue":"Applied Geography","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Public Health Agency of Canada","keywords":"Geography; Economies of agglomeration; Inequality; Population; Socioeconomics; Economic geography; Regression analysis; Regional science; Demographic economics; Economic growth; Agricultural economics; Demography; Sociology; Economics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003949239,0.00009897723,0.0001861029,0.00005164255,0.0002596873,0.00003815772,0.0001751412,0.00003135329,0.000004289583],"category_scores_gemma":[0.00001979237,0.0000752991,0.00003390503,0.0001493224,0.0003712999,0.0000756002,0.00003994989,0.00009801883,5.696028e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001393789,"about_ca_system_score_gemma":0.0004935042,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9630758,"about_ca_topic_score_gemma":0.9979904,"domain_scores_codex":[0.9990798,0.00005930176,0.0001854505,0.0001993038,0.000180899,0.0002952876],"domain_scores_gemma":[0.999258,0.0003632203,0.00009241811,0.0001413636,0.0000295237,0.0001154993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004272169,0.00001476128,0.9765276,0.00001163116,0.00004828617,0.00001124601,0.002992401,0.00001241668,0.000002488708,0.001272732,0.00008449996,0.01897927],"study_design_scores_gemma":[0.002549332,0.0001743284,0.8189023,0.00002899839,0.00007705046,7.372648e-7,0.07607019,0.00001904519,0.001343605,0.09521523,0.005182336,0.0004368855],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994002,0.001748973,8.184094e-7,0.00007176078,0.0001879517,0.0003308415,0.00002243215,0.00001116266,0.003624092],"genre_scores_gemma":[0.9996932,0.0001145133,0.000006972262,0.00004918971,0.00008115656,0.00004583106,0.000002232345,0.000005093444,0.00000186189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1576253,"threshold_uncertainty_score":0.3070608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01859723340879785,"score_gpt":0.2881652513143497,"score_spread":0.2695680179055518,"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."}}