{"id":"W2006825050","doi":"10.1371/journal.pone.0010911","title":"Community-Based Measures for Mitigating the 2009 H1N1 Pandemic in China","year":2010,"lang":"en","type":"article","venue":"PLoS ONE","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Program for New Century Excellent Talents in University; Natural Sciences and Engineering Research Council of Canada; Mitacs; National Natural Science Foundation of China; International Development Research Centre; University of Miami","keywords":"Outbreak; Pandemic; Mainland China; Basic reproduction number; Population; Psychological intervention; Transmission (telecommunications); Demography; Quarantine; China; Environmental health; Geography; Medicine; Disease; Infectious disease (medical specialty); Coronavirus disease 2019 (COVID-19); Virology; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003357374,0.0001323129,0.0003696258,0.0000303255,0.0003778803,0.00001550736,0.0003563771,0.00009967809,0.00002219806],"category_scores_gemma":[0.02829608,0.00008230671,0.00007688117,0.0001148993,0.0001411199,0.0000266564,0.0001024699,0.0009139045,0.000007364481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000367924,"about_ca_system_score_gemma":0.00002574068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002679917,"about_ca_topic_score_gemma":0.00264756,"domain_scores_codex":[0.998657,0.0004205659,0.0003271906,0.0001304028,0.0001913608,0.0002734889],"domain_scores_gemma":[0.9885799,0.01075779,0.000130035,0.000427653,0.00006627146,0.00003832525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001942668,0.006708915,0.7842176,0.002181933,0.0005601431,0.000003108044,0.00625748,0.00006697713,0.1676798,0.01934692,0.007578111,0.005204712],"study_design_scores_gemma":[0.002103247,0.0003135336,0.1379746,0.0006601317,0.0002801369,8.7448e-7,0.0004688765,0.006451007,0.01554236,0.8337889,0.001863352,0.0005529738],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914727,0.00006888969,0.0008192554,0.00638899,0.00002405101,0.0006370923,0.0000194129,0.0001205624,0.0004491095],"genre_scores_gemma":[0.9781608,0.000009711674,0.01971322,0.001711615,0.00008695076,0.000235318,0.000004682509,0.00001678908,0.00006093281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.814442,"threshold_uncertainty_score":0.979889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4238551209379022,"score_gpt":0.3991043860462896,"score_spread":0.02475073489161261,"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."}}