{"id":"W4384341577","doi":"10.31219/osf.io/a9ex5","title":"Death Seasonality, Google Community Mobility Trends, Seropositivity Rates, Comparisons of SINADEF Data with WHO Summary Data, and other Data Items as Useful in Analysis of Excess Deaths During the COVID-19 Pandemic in Peru, 2020-2021","year":2023,"lang":"en","type":"preprint","venue":"","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Government (linguistics); Seasonality; Public health; Geography; Tracking (education); Demography; Environmental health; Psychological intervention; Medicine; Statistics; Psychology; Sociology; Infectious disease (medical specialty); Disease","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":[0.000870839,0.0003926977,0.000287412,0.004649872,0.0002719297,0.000891884,0.0003088797,0.0002067696,0.01872258],"category_scores_gemma":[0.006921866,0.0001566195,0.0003598059,0.008027221,0.000153117,0.0007707425,0.0007567314,0.0003277542,0.00443313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000364908,"about_ca_system_score_gemma":0.0004523734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02197809,"about_ca_topic_score_gemma":0.03092643,"domain_scores_codex":[0.9994307,0.0001621707,0.00006833161,0.00008143977,0.0001943698,0.00006293839],"domain_scores_gemma":[0.9977342,0.0008275905,0.0005727431,0.0003186956,0.0004826975,0.00006416961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004218702,0.0001268172,0.6054333,0.001770538,0.0004014603,0.0003251968,0.002520592,0.003644769,0.00125412,0.00997797,0.2386825,0.1354408],"study_design_scores_gemma":[0.0000268868,0.00009816828,0.8324605,0.0001961267,0.00007726229,0.000403428,0.003039152,0.005192097,0.00163936,0.00315278,0.1536653,0.00004901975],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2143445,0.0007651,0.007055995,0.0008874232,0.0001960418,0.0002122606,0.7345864,0.001666897,0.04028536],"genre_scores_gemma":[0.4456705,0.001111832,0.01590567,0.00007003369,0.0001391404,0.0004047895,0.5208033,0.0004437547,0.01545099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02197809,"threshold_uncertainty_score":0.06263322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5632217944517814,"score_gpt":0.5028338508806673,"score_spread":0.0603879435711141,"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."}}