{"id":"W4389818049","doi":"10.2196/48738","title":"Triangulating Truth and Reaching Consensus on Population Size, Prevalence, and More: Modeling Study","year":2023,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Confidence interval; Preprint; Population; Metric (unit); Bayesian probability; Population size; Statistics; Small area estimation; Sample size determination; Stakeholder; Econometrics; Data mining; Artificial intelligence; Medicine; Mathematics; Environmental health; Engineering; World Wide Web; Operations management","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09086025,0.001291381,0.001687502,0.003565727,0.002054494,0.004605297,0.004770239,0.004096907,0.008392968],"category_scores_gemma":[0.3190207,0.001493137,0.004126223,0.003279487,0.003667301,0.005956883,0.006891387,0.003266766,0.001078727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005073496,"about_ca_system_score_gemma":0.004220573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02168283,"about_ca_topic_score_gemma":0.02056045,"domain_scores_codex":[0.9370642,0.05331708,0.001619791,0.004252034,0.002995502,0.0007513671],"domain_scores_gemma":[0.671083,0.2984045,0.009733173,0.01295588,0.006927757,0.0008955411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005348193,0.0002877994,0.05593931,0.002412729,0.001187512,0.0009843186,0.01970527,0.4206299,0.0007832098,0.3234073,0.01145377,0.162674],"study_design_scores_gemma":[0.0001760735,0.0002740636,0.006632818,0.001337837,0.0003737087,0.0003596452,0.002880266,0.6730399,0.0010357,0.2944158,0.01929449,0.000179665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05773639,0.0006813533,0.9230232,0.004187474,0.0001439534,0.001347037,0.001277924,0.001010136,0.01059261],"genre_scores_gemma":[0.398702,0.0004765351,0.5941337,0.0007349884,0.0000682758,0.002757646,0.0007827877,0.000288123,0.002055875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09086025,"threshold_uncertainty_score":0.4805207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06830879147203858,"score_gpt":0.37170279157136,"score_spread":0.3033940000993214,"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."}}