{"id":"W4200088307","doi":"10.4103/lungindia.lungindia_604_21","title":"Impact of COVID-19 pandemic on tuberculosis notifications in India","year":2021,"lang":"en","type":"letter","venue":"Lung India","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Tuberculosis; Pandemic; Quarter (Canadian coin); Coronavirus disease 2019 (COVID-19); Government (linguistics); Public health; Contact tracing; Confidence interval; Environmental health; Demography; Disease; Infectious disease (medical specialty); Geography","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","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.001058546,0.0005566308,0.001490527,0.0004809858,0.0001194925,0.00002860015,0.0005878244,0.001384768,0.0007007811],"category_scores_gemma":[0.01887515,0.0004407183,0.000666782,0.0007782145,0.0001900131,0.00004472161,0.0002522675,0.00245697,0.00006023196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002197563,"about_ca_system_score_gemma":0.0006174591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001048187,"about_ca_topic_score_gemma":0.0001668849,"domain_scores_codex":[0.9961014,0.0008014996,0.001129179,0.0008207457,0.0004814934,0.0006656729],"domain_scores_gemma":[0.9827102,0.01528884,0.0006918939,0.001066189,0.0000986021,0.0001443159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00001196608,0.0001215358,0.3751141,0.000717831,0.0003345314,0.0001411597,0.0006894396,0.0000423177,0.00001363915,0.0004271164,0.6223105,0.00007580363],"study_design_scores_gemma":[0.000928315,0.0002384721,0.8610164,0.0006820163,0.000432458,0.00002458713,0.00008981024,0.00007105418,0.00001055818,0.0293675,0.1059821,0.001156804],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.4111925,0.002303265,0.0008382194,0.5660276,0.001205696,0.004558855,0.003369656,0.0007623158,0.009741871],"genre_scores_gemma":[0.2971904,0.00107222,0.001644084,0.6939541,0.00192432,0.0008185825,0.001548491,0.000234253,0.001613634],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.5163285,"threshold_uncertainty_score":0.9999117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2180597705595746,"score_gpt":0.4479803663357839,"score_spread":0.2299205957762092,"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."}}