{"id":"W4309783298","doi":"10.5334/aogh.3903","title":"Geospatial Analysis of Dental Access and Workforce Distribution in Kenya","year":2022,"lang":"en","type":"article","venue":"Annals of Global Health","topic":"Dental Health and Care Utilization","field":"Dentistry","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Global Health Research","funders":"","keywords":"Workforce; Urbanization; Kenya; Population; Medicine; Geography; Environmental health; Socioeconomics; Economic growth; Political science","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":[0.0004558304,0.00008062109,0.000327973,0.00008959884,0.0001014104,0.00001189159,0.0001673425,0.00003673596,0.0001115915],"category_scores_gemma":[0.00003028946,0.00009267897,0.00008661975,0.001703734,0.00003962508,0.0001199743,0.0001757417,0.00008451077,0.000001148786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001907784,"about_ca_system_score_gemma":0.0001502786,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008115482,"about_ca_topic_score_gemma":0.02355677,"domain_scores_codex":[0.9984534,0.0001651256,0.0005261855,0.0001962155,0.0003973982,0.0002616133],"domain_scores_gemma":[0.9993004,0.00002663203,0.0003302463,0.0001462191,0.00007084491,0.0001256889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000247665,0.0001705905,0.961239,0.0001068236,0.00004090838,0.000006271241,0.00003730602,0.0006222315,0.000001185527,0.002968971,0.001909515,0.03264953],"study_design_scores_gemma":[0.0003073073,0.0001883709,0.9963812,0.00001992628,0.00002888086,0.000007366146,0.000352413,0.001744756,0.00002666273,0.0003311493,0.0005528851,0.00005906773],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951361,0.001260557,0.000352137,0.0001626949,0.0002154139,0.0001876257,0.002377338,0.00001107086,0.0002969994],"genre_scores_gemma":[0.9981641,0.0002184706,0.000006394355,0.0005567385,0.0000137865,0.00000907527,0.001008613,0.0000034717,0.00001935634],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03514222,"threshold_uncertainty_score":0.9984896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04105149856624556,"score_gpt":0.4205260955861108,"score_spread":0.3794745970198652,"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."}}