{"id":"W2953828951","doi":"10.1590/0102-311x00141218","title":"Health profile differences between recipients and non-recipients of the Brazilian Income Transfer Program in a low-income population","year":2019,"lang":"en","type":"article","venue":"Cadernos de Saúde Pública","topic":"Poverty, Education, and Child Welfare","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Per capita; Environmental health; Transfer payment; Population; Demography; Medicine; Conditional cash transfer; Public health; Per capita income; Gerontology; Poverty; Economics; Economic growth","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":[0.0005159992,0.0001411238,0.0003083827,0.000121584,0.0003160036,0.00005479119,0.0004047245,0.0001600282,0.00008426536],"category_scores_gemma":[0.00007145183,0.0001112578,0.00007071782,0.0005711836,0.0001597878,0.0002253905,0.00003563301,0.0002284351,0.000010436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002259882,"about_ca_system_score_gemma":0.0003254227,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008281508,"about_ca_topic_score_gemma":0.005137667,"domain_scores_codex":[0.9981362,0.0003089279,0.0003871312,0.0003045395,0.0004227891,0.0004403656],"domain_scores_gemma":[0.99929,0.00009417256,0.0001266425,0.0002501685,0.00005641509,0.0001826084],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009138228,0.0001043641,0.9727931,0.00006887089,0.000009118061,5.505418e-8,0.01231351,5.622431e-7,0.000005689664,0.0003249646,0.00003379705,0.01433684],"study_design_scores_gemma":[0.0004196229,0.00007843441,0.9963427,0.0003092421,0.000007735852,2.469984e-7,0.0005634518,0.0000277631,0.00002719012,0.0005754849,0.001521556,0.0001266133],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991942,0.0001280518,0.000007088158,0.004354689,0.0003346335,0.001328134,0.00004814051,0.00003583912,0.001821383],"genre_scores_gemma":[0.9989786,0.0001154104,0.00006298186,0.0001433412,0.0001277056,0.00005242107,0.00002664747,0.00001360194,0.0004793553],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02354956,"threshold_uncertainty_score":0.9983224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01390563648538862,"score_gpt":0.301325258215156,"score_spread":0.2874196217297674,"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."}}