{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003882665,0.0002304006,0.0001864669,0.005008644,0.0006192842,0.0007345305,0.0002742344,0.0001976015,0.002506995],"category_scores_gemma":[0.001542838,0.0002101923,0.0003414075,0.006881225,0.000247075,0.0004163742,0.0008048655,0.0001682889,0.0002485467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001153525,"about_ca_system_score_gemma":0.001388641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1719219,"about_ca_topic_score_gemma":0.2479341,"domain_scores_codex":[0.9996537,0.0001147597,0.00003946953,0.00004868996,0.00007957378,0.00006375344],"domain_scores_gemma":[0.9994767,0.000152711,0.0001726585,0.00002865176,0.0001336663,0.00003555603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001103338,0.00008018849,0.9463458,0.0004315488,0.0001485237,0.0006455458,0.003220825,0.004671895,0.001376467,0.00212785,0.004591851,0.03624918],"study_design_scores_gemma":[0.00001131567,0.00002994809,0.9757319,0.0001994726,0.00006030636,0.0002303463,0.008329369,0.007502955,0.0002720855,0.0003085741,0.007299354,0.00002441654],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805595,0.0006132512,0.0009626409,0.0002672777,0.000008966828,0.0001213567,0.0127025,0.00004575981,0.004718635],"genre_scores_gemma":[0.9904266,0.0005029968,0.003416113,0.00002375183,0.000004101654,0.0001125625,0.004982225,0.000008095378,0.0005235864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1719219,"threshold_uncertainty_score":0.3418425,"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."}}