{"id":"W3031235644","doi":"10.1101/2020.05.26.20113456","title":"Burden of COVID-19 pandemic in India: Perspectives from Health Infrastructure","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lakh; Case fatality rate; Pandemic; Population; Procurement; Coronavirus disease 2019 (COVID-19); Test (biology); Quarter (Canadian coin); Public health; Health care; Medicine; Business; Geography; Socioeconomics; Environmental health; Economic growth; Agriculture; Economics; Nursing","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001447868,0.0004873536,0.002013784,0.0001743165,0.00007650989,0.00001527564,0.0008166633,0.0005562165,0.0003015217],"category_scores_gemma":[0.03265052,0.0003954078,0.0002829281,0.0003090492,0.0002825295,0.00002735384,0.001918636,0.0017751,0.0000108507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001136633,"about_ca_system_score_gemma":0.000845806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005306944,"about_ca_topic_score_gemma":0.0004127793,"domain_scores_codex":[0.9959494,0.0009544993,0.0011916,0.001059281,0.0004083542,0.000436811],"domain_scores_gemma":[0.9922718,0.005514792,0.001087231,0.000725463,0.00007315593,0.0003275873],"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":[0.00009380464,0.00008633341,0.920575,0.002212029,0.0003196672,0.00004544746,0.06197383,0.0009863708,0.0001324659,0.007127343,0.005867524,0.0005801965],"study_design_scores_gemma":[0.0004788015,0.00006637446,0.3453345,0.0002239533,0.00004052493,0.000001292826,0.00405473,0.000205769,0.000006313936,0.6466329,0.002641731,0.0003131922],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9640365,0.005985853,0.004159736,0.02326059,0.0002871943,0.001182944,0.0005096403,0.0002921149,0.0002854877],"genre_scores_gemma":[0.9880353,0.002167548,0.007171806,0.002041556,0.0003896989,0.00009232139,0.00004059611,0.00004228449,0.0000188598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6395055,"threshold_uncertainty_score":0.9998498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2142102814029623,"score_gpt":0.4406636620395921,"score_spread":0.2264533806366297,"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."}}