{"id":"W4229056739","doi":"10.1371/journal.pone.0267232","title":"The impact of the government response on pandemic control in the long run—A dynamic empirical analysis based on COVID-19","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Case fatality rate; Government (linguistics); Transmission (telecommunications); Pandemic; Scale (ratio); Development economics; Economics; Coronavirus disease 2019 (COVID-19); Geography; Medicine; Environmental health; Disease; Engineering; Population","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004658171,0.0003961152,0.0006195856,0.001211836,0.000598056,0.002022923,0.0008539521,0.001554996,0.003018922],"category_scores_gemma":[0.02053371,0.0002468428,0.0009370865,0.00136552,0.001224908,0.001888185,0.001583985,0.00256874,0.000334105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001639011,"about_ca_system_score_gemma":0.001005596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01303294,"about_ca_topic_score_gemma":0.007896626,"domain_scores_codex":[0.9964927,0.001610506,0.0001999497,0.0005925342,0.0003342876,0.0007701065],"domain_scores_gemma":[0.9764631,0.01220854,0.007890974,0.0008211096,0.001207466,0.001408799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003776753,0.000311369,0.9275268,0.0001328201,0.0007078286,0.00120484,0.0009901224,0.04813223,0.0007523763,0.007909367,0.001675555,0.01027893],"study_design_scores_gemma":[0.00005674045,0.0006663033,0.7822204,0.0001001622,0.0003831283,0.0002813186,0.004544663,0.2017254,0.0007191229,0.006216066,0.002992532,0.0000942083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941984,0.0004454022,0.001556667,0.001426844,0.00002638476,0.00003658972,0.0003138121,0.00001546695,0.001980403],"genre_scores_gemma":[0.9992566,0.00008670015,0.0001101188,0.00007088391,0.00001471271,0.00001247167,0.0001389133,0.000003065041,0.0003065096],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01303294,"threshold_uncertainty_score":0.02591419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2538775238146339,"score_gpt":0.4355638734661961,"score_spread":0.1816863496515622,"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."}}