{"id":"W4417297143","doi":"10.1371/journal.pdig.0000782","title":"COVID-19 vaccination data management and visualization systems for improved decision-making: Lessons learnt from Africa CDC Saving Lives and Livelihoods program","year":2025,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Centers for Disease Control and Prevention; Mastercard Foundation","keywords":"Dashboard; Vaccination; Context (archaeology); Public health; Data visualization; Information system; Visualization; Health informatics; Tracking (education)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004464431,0.000118742,0.0002159437,0.0001345381,0.0007725662,0.0007621288,0.0002252926,0.00005806925,0.000005563114],"category_scores_gemma":[0.001155205,0.0001174161,0.00002046777,0.000274785,0.00001323187,0.0007541787,0.0002389621,0.00005577121,0.000001176035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002313281,"about_ca_system_score_gemma":0.0003893709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000479283,"about_ca_topic_score_gemma":0.0009131841,"domain_scores_codex":[0.9986598,0.00008023479,0.0003097257,0.0004724539,0.0001861731,0.0002916282],"domain_scores_gemma":[0.9986064,0.0007051696,0.000158966,0.0002397012,0.00008137937,0.0002083605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000130865,0.0005522931,0.011307,0.001330624,0.0001244543,0.000002967721,0.01072086,0.000008767019,0.000002044958,0.04344728,0.008287309,0.9240856],"study_design_scores_gemma":[0.00522259,0.001605988,0.09003916,0.004465522,0.0003609018,0.000002991293,0.07396495,0.1079123,0.000003533122,0.1058395,0.6091869,0.0013956],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2009049,0.07589925,0.625371,0.04054365,0.001744626,0.02706342,0.003469818,0.002010598,0.02299274],"genre_scores_gemma":[0.9949436,0.001996743,0.001963186,0.0004218837,0.0001012869,0.0001080465,0.0001602474,0.00001362647,0.0002913735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9226899,"threshold_uncertainty_score":0.7349226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07647059303465331,"score_gpt":0.4197332053094203,"score_spread":0.343262612274767,"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."}}